diff --git a/.gitattributes b/.gitattributes
index a6344aac8c09253b3b630fb776ae94478aa0275b..79ace35d542ecc6e20d6df974bb3c4ecf2aef87d 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+huggan/assets/cyclegan.png filter=lfs diff=lfs merge=lfs -text
+huggan/assets/lightweight_gan_wandb.png filter=lfs diff=lfs merge=lfs -text
+huggan/assets/pix2pix_maps.png filter=lfs diff=lfs merge=lfs -text
+huggan/assets/wandb.png filter=lfs diff=lfs merge=lfs -text
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..b5a276da1d4bb9cc5fab31914aaaebcac1422a8c
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,166 @@
+# Initially taken from Github's Python gitignore file
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# tests and logs
+tests/fixtures/cached_*_text.txt
+logs/
+lightning_logs/
+lang_code_data/
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# vscode
+.vs
+.vscode
+
+# Pycharm
+.idea
+
+# TF code
+tensorflow_code
+
+# Models
+proc_data
+
+# examples
+runs
+/runs_old
+/wandb
+/examples/runs
+/examples/**/*.args
+/examples/rag/sweep
+
+# data
+/data
+serialization_dir
+
+# emacs
+*.*~
+debug.env
+
+# vim
+.*.swp
+
+#ctags
+tags
+
+# pre-commit
+.pre-commit*
+
+# .lock
+*.lock
+
+# DS_Store (MacOS)
+.DS_Store
diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..28efb1a78cb9a972d83a99a0e5d8198888f5d478
--- /dev/null
+++ b/README.md
@@ -0,0 +1,16 @@
+# Community Events @ 🤗
+
+A central repository for all community events organized by 🤗 HuggingFace. Come one, come all!
+We're constantly finding ways to democratise the use of ML across modalities and languages. This repo contains information about all past, present and upcoming events.
+
+## Hugging Events
+
+| **Event Name**                                                          | **Dates**       | **Status**                                                                                                   |
+|-------------------------------------------------------------------------|-----------------|--------------------------------------------------------------------------------------------------------------|
+| [Open Source AI Game Jam 🎮 (First Edition)](/open-source-ai-game-jam)               | July 7th - 9th, 2023  | Finished |
+| [Whisper Fine Tuning Event](/whisper-fine-tuning-event)               | Dec 5th - 19th, 2022  | Finished |
+| [Computer Vision Study Group](/computer-vision-study-group)             | Ongoing         | Monthly                                                                                                      |
+| [ML for Audio Study Group](https://github.com/Vaibhavs10/ml-with-audio) | Ongoing         | Monthly                                                                                                      |
+| [Gradio Blocks](/gradio-blocks)                                         | May 16th - 31st, 2022 | Finished                                                                                                     |
+| [HugGAN](/huggan)                                                       | Apr 4th - 17th, 2022  | Finished                                                                                                     |
+| [Keras Sprint](keras-sprint)                                            | June, 2022            | Finished                                                                                                     |
diff --git a/added_tokens.json b/added_tokens.json
new file mode 100644
index 0000000000000000000000000000000000000000..e3d256c988462aa153dcabe2aa38b8e9b436c06f
--- /dev/null
+++ b/added_tokens.json
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diff --git a/computer-vision-study-group/Notebooks/HuggingFace_vision_ecosystem_overview_(June_2022).ipynb b/computer-vision-study-group/Notebooks/HuggingFace_vision_ecosystem_overview_(June_2022).ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..d5c70472d4a23547a0590fa9d7bca8c4a094629a
--- /dev/null
+++ b/computer-vision-study-group/Notebooks/HuggingFace_vision_ecosystem_overview_(June_2022).ipynb
@@ -0,0 +1,24097 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nvpX6LUMayKp"
+      },
+      "source": [
+        "# HuggingFace vision ecosystem: overview (June 2022)\n",
+        "\n",
+        "In this notebook, we'll go over the tools HuggingFace has available for computer vision and multi-modal models that involve vision.\n",
+        "\n",
+        "## Set-up environment\n",
+        "\n",
+        "We'll first install the required libraries: 🤗 [Transformers](https://github.com/huggingface/transformers) and 🤗 [Datasets](https://github.com/huggingface/datasets)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "g58FC0VGauCi",
+        "outputId": "9f9116b3-93a8-4cb6-fcf1-22b76fd34e67"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[K     |████████████████████████████████| 4.2 MB 5.0 MB/s \n",
+            "\u001b[K     |████████████████████████████████| 346 kB 44.6 MB/s \n",
+            "\u001b[K     |████████████████████████████████| 6.6 MB 30.9 MB/s \n",
+            "\u001b[K     |████████████████████████████████| 596 kB 69.1 MB/s \n",
+            "\u001b[K     |████████████████████████████████| 86 kB 5.2 MB/s \n",
+            "\u001b[K     |████████████████████████████████| 212 kB 51.2 MB/s \n",
+            "\u001b[K     |████████████████████████████████| 140 kB 67.2 MB/s \n",
+            "\u001b[K     |████████████████████████████████| 86 kB 4.7 MB/s \n",
+            "\u001b[K     |████████████████████████████████| 127 kB 72.5 MB/s \n",
+            "\u001b[K     |████████████████████████████████| 112 kB 52.8 MB/s \n",
+            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
+            "\u001b[?25h"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install -q transformers datasets"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0IM9MGmT2e4R"
+      },
+      "source": [
+        "We'll also install [timm](https://github.com/rwightman/pytorch-image-models) as we'll showcase [DETR](https://huggingface.co/docs/transformers/model_doc/detr) (a Transformer-based object detection model)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "887fBd5r2g-T",
+        "outputId": "45f55b11-0830-4277-89b5-419f5b1e9020"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[?25l\r\u001b[K     |▊                               | 10 kB 19.9 MB/s eta 0:00:01\r\u001b[K     |█▌                              | 20 kB 12.9 MB/s eta 0:00:01\r\u001b[K     |██▎                             | 30 kB 8.0 MB/s eta 0:00:01\r\u001b[K     |███                             | 40 kB 7.3 MB/s eta 0:00:01\r\u001b[K     |███▉                            | 51 kB 3.5 MB/s eta 0:00:01\r\u001b[K     |████▋                           | 61 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |█████▎                          | 71 kB 4.7 MB/s eta 0:00:01\r\u001b[K     |██████                          | 81 kB 5.1 MB/s eta 0:00:01\r\u001b[K     |██████▉                         | 92 kB 5.7 MB/s eta 0:00:01\r\u001b[K     |███████▋                        | 102 kB 3.7 MB/s eta 0:00:01\r\u001b[K     |████████▍                       | 112 kB 3.7 MB/s eta 0:00:01\r\u001b[K     |█████████▏                      | 122 kB 3.7 MB/s eta 0:00:01\r\u001b[K     |█████████▉                      | 133 kB 3.7 MB/s eta 0:00:01\r\u001b[K     |██████████▋                     | 143 kB 3.7 MB/s eta 0:00:01\r\u001b[K     |███████████▍                    | 153 kB 3.7 MB/s eta 0:00:01\r\u001b[K     |████████████▏                   | 163 kB 3.7 MB/s eta 0:00:01\r\u001b[K     |█████████████                   | 174 kB 3.7 MB/s eta 0:00:01\r\u001b[K     |█████████████▊                  | 184 kB 3.7 MB/s eta 0:00:01\r\u001b[K     |██████████████▍                 | 194 kB 3.7 MB/s eta 0:00:01\r\u001b[K     |███████████████▏                | 204 kB 3.7 MB/s eta 0:00:01\r\u001b[K     |████████████████                | 215 kB 3.7 MB/s eta 0:00:01\r\u001b[K     |████████████████▊               | 225 kB 3.7 MB/s eta 0:00:01\r\u001b[K     |█████████████████▌              | 235 kB 3.7 MB/s eta 0:00:01\r\u001b[K     |██████████████████▎             | 245 kB 3.7 MB/s eta 0:00:01\r\u001b[K     |███████████████████             | 256 kB 3.7 MB/s eta 0:00:01\r\u001b[K     |███████████████████▊            | 266 kB 3.7 MB/s eta 0:00:01\r\u001b[K     |████████████████████▌           | 276 kB 3.7 MB/s eta 0:00:01\r\u001b[K     |█████████████████████▎          | 286 kB 3.7 MB/s eta 0:00:01\r\u001b[K     |██████████████████████          | 296 kB 3.7 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▉         | 307 kB 3.7 MB/s eta 0:00:01\r\u001b[K     |███████████████████████▌        | 317 kB 3.7 MB/s eta 0:00:01\r\u001b[K     |████████████████████████▎       | 327 kB 3.7 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████       | 337 kB 3.7 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▉      | 348 kB 3.7 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▋     | 358 kB 3.7 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▍    | 368 kB 3.7 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████    | 378 kB 3.7 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▉   | 389 kB 3.7 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▋  | 399 kB 3.7 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▍ | 409 kB 3.7 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▏| 419 kB 3.7 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 430 kB 3.7 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 431 kB 3.7 MB/s \n",
+            "\u001b[?25h"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install -q timm"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "myjaD2FKa09o"
+      },
+      "source": [
+        "## Models\n",
+        "\n",
+        "First of all, 🤗 Transformers currently includes a large amount of vision models, for various tasks. Let's first load a Vision Transformer (ViT). In 2020, [Google showed](https://arxiv.org/abs/2010.11929) that a Transformer encoder (BERT-like) can obtain state-of-the-art performance on ImageNet.\n",
+        "\n",
+        "In 2022, it was [shown](https://arxiv.org/abs/2205.01580) that, with a few improvements, ViTs can obtain the same performance on ImageNet as a ResNet-50 given the same training time and compute.\n",
+        "\n",
+        "### Load a model\n",
+        "\n",
+        "Instantiating a model without pre-trained weights can be done by 1) instantiating a configuration, defining the model architecture 2) creating a model based on that configuration."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "0Shm7J7XaxyR"
+      },
+      "outputs": [],
+      "source": [
+        "from transformers import ViTConfig, ViTForImageClassification\n",
+        "\n",
+        "# option 1: load with randomly initialized weights (train from scratch)\n",
+        "\n",
+        "config = ViTConfig(num_hidden_layers=12, hidden_size=768)\n",
+        "model = ViTForImageClassification(config)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iX4yDa2ccM_p"
+      },
+      "source": [
+        "The configuration just stores the hyperparameters related to the architecture of the model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "yCPXyQc7vnWc",
+        "outputId": "a9f3bae5-4144-49be-d7e2-67e72d505cee"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "ViTConfig {\n",
+            "  \"attention_probs_dropout_prob\": 0.0,\n",
+            "  \"encoder_stride\": 16,\n",
+            "  \"hidden_act\": \"gelu\",\n",
+            "  \"hidden_dropout_prob\": 0.0,\n",
+            "  \"hidden_size\": 768,\n",
+            "  \"image_size\": 224,\n",
+            "  \"initializer_range\": 0.02,\n",
+            "  \"intermediate_size\": 3072,\n",
+            "  \"layer_norm_eps\": 1e-12,\n",
+            "  \"model_type\": \"vit\",\n",
+            "  \"num_attention_heads\": 12,\n",
+            "  \"num_channels\": 3,\n",
+            "  \"num_hidden_layers\": 12,\n",
+            "  \"patch_size\": 16,\n",
+            "  \"qkv_bias\": true,\n",
+            "  \"transformers_version\": \"4.19.2\"\n",
+            "}\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(config)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "w8xWxsk_cRxw"
+      },
+      "source": [
+        "Alternatively, (and this is what most people use), is to equip a model with pre-trained weights, such that it can be easily fine-tuned on a custom dataset. This is the power of transfer learning: typically one pre-trains a model (like ViT) on a huge amount of data (like ImageNet), after which one can just place a linear layer on top of the model for fine-tuning it on a custom dataset. \n",
+        "\n",
+        "Let's load the weights of this model: https://huggingface.co/google/vit-base-patch16-224"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 70,
+      "metadata": {
+        "id": "ypogFy89a6TG"
+      },
+      "outputs": [],
+      "source": [
+        "# option 2: load with pretrained weights (fine-tune)\n",
+        "# choose any repo from the hub!\n",
+        "\n",
+        "model = ViTForImageClassification.from_pretrained(\"google/vit-base-patch16-224\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "16fezyb2zyFi"
+      },
+      "source": [
+        "Note that ANY model (and any dataset, Space) on the hub has its own Git repo with its own Git history. This means that you can always refer to a specific **revision** (like a branch or commit hash).\n",
+        "\n",
+        "Simply go to the \"commit history\" of [this model](https://huggingface.co/google/vit-base-patch16-224) for instance (\"files and version\" -> \"history\"), and copy-paste a commit hash."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 81,
+          "referenced_widgets": [
+            "243d4938860942b2ba1024ed10f8b907",
+            "905ff301192d4bb4a401ef06d70a9189",
+            "cd19543310b443b99946a64ace409659",
+            "76ef27ff545f443c90abbf5e17008240",
+            "e2ce97d00cb44a7da48bb439f95791c2",
+            "46d657c2bbb94a858ec54036ae2d93b7",
+            "17bd99f8e19e47c3af0b308027afad2c",
+            "b5ba4d7eedc7407c8f70ecd4a4ad41e0",
+            "7e32c676253f4882b76e3172ae39bbac",
+            "d25c2d1b17584164a3859e4d0e23880c",
+            "81575bd8d7a14f2c9343e3cf61c3805f",
+            "59b004c7072546fb8057b87f10dff707",
+            "25b22f473af346d8a055fd180156b747",
+            "b0e67980e60b468ca10ba9cae670a01d",
+            "ef20339664d5460fa951b1e3567cf6f2",
+            "97882337019149d09b76780e30f20525",
+            "5b8ccd69d5c24be893b396cc16b6edee",
+            "cefbb2bedb574363addf54a368287489",
+            "866b6114ad06416890378ce87082286b",
+            "0c709e38072a4308b7e7d6bef5663d01",
+            "fd54fad2ef214c46900f15dca5742bfb",
+            "80ecdaabd84a445dbd549fe87cb4dc30"
+          ]
+        },
+        "id": "9dK6-UVR0JYq",
+        "outputId": "94637c0d-7572-4dd3-8a61-0916dc6d71e3"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "243d4938860942b2ba1024ed10f8b907",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/68.0k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "59b004c7072546fb8057b87f10dff707",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/330M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "model = ViTForImageClassification.from_pretrained(\"google/vit-base-patch16-224\",\n",
+        "                                                  revision=\"db75733ce9ead4ed3dce26ab87a6ed2f6f565985\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pQVQlClzgtAB"
+      },
+      "source": [
+        "You can also load a ViT model in another framework (like TensorFlow or JAX/FLAX):"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 140,
+          "referenced_widgets": [
+            "68affdcf9c30442cb1b59ff4c2a57975",
+            "79413972bc3f4bd3b3411b5fb5da4a6f",
+            "232dc3949156475c9164271a3a0e2deb",
+            "99010785c2cb42a98dcd33e5a908cb72",
+            "5ab1f70cb16b4d7ab09628e4a68eaacb",
+            "73d9b9262b5849798b78f337ea557e62",
+            "21c17d6c96f54beeab9620e8ca56a744",
+            "e9de8db310384e50804f1612afb02e5c",
+            "3e3a75c3e7a1435bbbbfb1b6e1bb89b3",
+            "1c31b382c0044c128e3f9688a4a0cec4",
+            "d11e1bc7cc55414994189037eeb67f21"
+          ]
+        },
+        "id": "RRD1ixa5gu3z",
+        "outputId": "9e0427bb-45af-487c-d4e0-2aeccd1728c8"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "68affdcf9c30442cb1b59ff4c2a57975",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/330M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "All model checkpoint layers were used when initializing TFViTForImageClassification.\n",
+            "\n",
+            "All the layers of TFViTForImageClassification were initialized from the model checkpoint at google/vit-base-patch16-224.\n",
+            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFViTForImageClassification for predictions without further training.\n"
+          ]
+        }
+      ],
+      "source": [
+        "from transformers import TFViTForImageClassification\n",
+        "\n",
+        "model = TFViTForImageClassification.from_pretrained(\"google/vit-base-patch16-224\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xkZP45lZg4K1"
+      },
+      "source": [
+        "We are adding support for other frameworks for each model (as 🤗 Transformers is a framework-agnostic library). Models are typically first added in PyTorch, after which they are ported to other frameworks. Models supported by TensorFlow (at the time of writing) include: ViT, BEiT, ConvNeXT, Swin Transformer and Data2VecVision."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uerYNyqQ0LEQ"
+      },
+      "source": [
+        "### Private models\n",
+        "\n",
+        "Note that models, datasets and Spaces could also be hosted on a private hub. This means that you get the same functionality as the public HuggingFace hub, but everything is only shared with a particular organization. This is ideal if you want to build and share ML models inside a company, for instance.\n",
+        "\n",
+        "See details on private hub: https://huggingface.co/platform"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "D8Fgon3911UU"
+      },
+      "source": [
+        "## Pushing a model to the hub\n",
+        "\n",
+        "Pushing a model to the hub can be done using `model.push_to_hub`. Docs can be found here: https://huggingface.co/docs/transformers/v4.19.2/en/main_classes/model#transformers.PreTrainedModel.push_to_hub"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "id": "ksoV-SQOwh59"
+      },
+      "outputs": [],
+      "source": [
+        "# let's create a small ViT model\n",
+        "config = ViTConfig(num_hidden_layers=2, hidden_size=12)\n",
+        "model = ViTForImageClassification(config)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qVCJoDRXrFbr",
+        "outputId": "b7cec7bf-08c3-442c-948c-c21de3a62890"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "        _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
+            "        _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
+            "        _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
+            "        _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
+            "        _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
+            "\n",
+            "        To login, `huggingface_hub` now requires a token generated from https://huggingface.co/settings/tokens .\n",
+            "        (Deprecated, will be removed in v0.3.0) To login with username and password instead, interrupt with Ctrl+C.\n",
+            "        \n",
+            "Token: \n",
+            "Login successful\n",
+            "Your token has been saved to /root/.huggingface/token\n",
+            "\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n",
+            "You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n",
+            "\n",
+            "git config --global credential.helper store\u001b[0m\n"
+          ]
+        }
+      ],
+      "source": [
+        "# authenticate with the hub by providing our token\n",
+        "!huggingface-cli login"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 266,
+          "referenced_widgets": [
+            "aed53a3ced8e4751934297ee6b3a919f",
+            "c6714f85f70e4fd3905f4a839ab59310",
+            "ae5fa4a74652418ea28c8ac247b7bf23",
+            "305fa41ab72d487aa5253ca89160bb63",
+            "d8e7626a8e1b4ff5a12093a11bd80014",
+            "5ae199a168d34fecbfa2cf3cfda8e964",
+            "7ff1990838a44511954efb6cdc0345f3",
+            "9fc8b7910a3c4ea1980b64fa86b6f001",
+            "fb7af1be9e9a41fcb320c0f1b5529611",
+            "132d8068687a436eb3ab84bdc04afbe9",
+            "307979d68f5647658b7aebfe3fef6857"
+          ]
+        },
+        "id": "G9ceJHPeOV5I",
+        "outputId": "f6499752-dbf5-4f6d-c67b-0d33efbefcb2"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/huggingface_hub/utils/_deprecation.py:43: FutureWarning: Pass token='vit-demo' as keyword args. From version 0.8 passing these as positional arguments will result in an error\n",
+            "  FutureWarning,\n",
+            "/usr/local/lib/python3.7/dist-packages/huggingface_hub/hf_api.py:676: FutureWarning: `create_repo` now takes `token` as an optional positional argument. Be sure to adapt your code!\n",
+            "  FutureWarning,\n",
+            "Cloning https://huggingface.co/nielsr/vit-demo into local empty directory.\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "aed53a3ced8e4751934297ee6b3a919f",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Upload file pytorch_model.bin:   1%|          | 3.34k/662k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "remote: Enforcing permissions...        \n",
+            "remote: Allowed refs: all        \n",
+            "To https://huggingface.co/nielsr/vit-demo\n",
+            "   22a7a98..e82e16f  main -> main\n",
+            "\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            },
+            "text/plain": [
+              "'https://huggingface.co/nielsr/vit-demo/commit/e82e16fbca2637e6bd22468e1d5f5de5db03268b'"
+            ]
+          },
+          "execution_count": 17,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "model.push_to_hub(\"niels/vit-demo\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 101,
+      "metadata": {
+        "id": "jYcSlheG0NqN"
+      },
+      "outputs": [],
+      "source": [
+        "# reload back again\n",
+        "model = ViTForImageClassification.from_pretrained(\"niels/vit-demo\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WlxKE83abc2H"
+      },
+      "source": [
+        "### Load a feature extractor\n",
+        "\n",
+        "A feature extractor can be used to prepare images for the model.\n",
+        "\n",
+        "It's a minimal object to prepare images for inference.\n",
+        "\n",
+        "It typically does some very simple image transformations (like resizing to fixed size + normalizing)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 66,
+      "metadata": {
+        "id": "eXAfeDMubefP"
+      },
+      "outputs": [],
+      "source": [
+        "from transformers import ViTFeatureExtractor\n",
+        "\n",
+        "feature_extractor = ViTFeatureExtractor()\n",
+        "\n",
+        "# or, to load one that corresponds to a checkpoint on the hub:\n",
+        "feature_extractor = ViTFeatureExtractor.from_pretrained(\"google/vit-base-patch16-224\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wTj-RYgtbaLC"
+      },
+      "source": [
+        "### Predict on image\n",
+        "\n",
+        "Let's load a cats image of the COCO dataset."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 497
+        },
+        "id": "-bGDZaKcbbVi",
+        "outputId": "42c7b80f-30ab-40a3-d7ac-c5812f8912e9"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480 at 0x7F79AB709450>"
+            ]
+          },
+          "execution_count": 21,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from PIL import Image\n",
+        "import requests\n",
+        "\n",
+        "url = 'http://images.cocodataset.org/val2017/000000039769.jpg'\n",
+        "image = Image.open(requests.get(url, stream=True).raw)\n",
+        "image.save(\"cats.png\")\n",
+        "image"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "GelehaYFbqY6",
+        "outputId": "2b39281b-cee1-4f66-c8c0-40d7179fac23"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "torch.Size([1, 3, 224, 224])\n"
+          ]
+        }
+      ],
+      "source": [
+        "# prepare for the model\n",
+        "inputs = feature_extractor(image, return_tensors=\"pt\")\n",
+        "pixel_values = inputs.pixel_values\n",
+        "print(pixel_values.shape)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "HGWRHvYsbvcB"
+      },
+      "outputs": [],
+      "source": [
+        "# forward pass\n",
+        "outputs = model(pixel_values)\n",
+        "logits = outputs.logits"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "UpiFj8a4byWC",
+        "outputId": "613905dc-bfad-4f2c-cc5f-eb5da8cff0e1"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Predicted class idx: 282\n"
+          ]
+        }
+      ],
+      "source": [
+        "# take argmax on logits' last dimension\n",
+        "predicted_class_idx = logits.argmax(-1).item()\n",
+        "# turn into actual class name\n",
+        "print(model.config.id2label[predicted_class_idx])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RYXXspm0bX-8"
+      },
+      "source": [
+        "### Auto API\n",
+        "\n",
+        "The [Auto Classes](https://huggingface.co/docs/transformers/main/en/model_doc/auto) automatically instantiate the appropriate class for you, based on the checkpoint identifier you provide.\n",
+        "\n",
+        "Currently includes Auto classes for [image classification](https://huggingface.co/docs/transformers/main/en/model_doc/auto#transformers.AutoModelForImageClassification), [object detection](https://huggingface.co/docs/transformers/main/en/model_doc/auto#transformers.AutoModelForObjectDetection), [image segmentation](https://huggingface.co/docs/transformers/main/en/model_doc/auto#transformers.AutoModelForImageSegmentation), and [vision2seq](https://huggingface.co/docs/transformers/main/en/model_doc/auto#transformers.AutoModelForVision2Seq)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 113,
+          "referenced_widgets": [
+            "247bc93b19724c2eae38b148ebd36faa",
+            "c5e6eb48578544da9960151bd4261fa9",
+            "904466f89d014246b06aa8c202ae91eb",
+            "4fd65d6a96ac4c08bf2cfa07b1f0d4c6",
+            "dcae9ec41c424214863a94641c6c2793",
+            "5f3b0e2b24294026aec08c561d3088c8",
+            "9661dd3c3c604622aadf3e1d147343ac",
+            "92a94bb63800496c8cfe08469c6c4f9b",
+            "ecc7130759004c40843042a4c88dd3e7",
+            "ac07fbbbbf0042e28b0a65c0bebe5c53",
+            "d37c7e1da4804c74b2cd3cdcb712518f",
+            "4c23fc80cfa945e3ab8fbc0cefeaa88a",
+            "6bf41e507f0d4bdab0718f7cfb0f64ab",
+            "8dd9bfa6be1847d8bcfda4e58a79a49b",
+            "b0197fa6a73149178095b1b0d4ca912f",
+            "e22aea73562e4fd7af59c86d199c9d06",
+            "4b9040d3b5fc4aef9870749953f6f3dc",
+            "b2e398d8b4c14e13b392526a30ad9645",
+            "d7e16d6695c94875bd1cdb5ef91130ed",
+            "da65e6e1f29e43efa94b138835ff08ae",
+            "f69c2b33c0c74a76b4e36b78f8769ebf",
+            "b16dd6b230c7417d9fe545ea2b61c6c4",
+            "f41d6117df6e486b8f94e23b21a956a5",
+            "d771e016a16b447b8f8cae13c2c6d66f",
+            "95e416b00e6042b2a308b90160dad073",
+            "d89a0e73bab44f1abb85816622d54b39",
+            "ad035a8f5660494ea8f87799d873a6b7",
+            "2c9e40c42ffa4a38afb20b171565be06",
+            "c9d8589bddc74d4b93cba6faca7f1f2f",
+            "1071d53bb40d4d62bce36f6f9061e287",
+            "48dbe8e2192e4fa3b4017501cdcba264",
+            "bb43e8c2b0224a039fb7da54cd56774f",
+            "4f33f61dd58f4366a5a133f53768c057"
+          ]
+        },
+        "id": "9U__X2vhbZB0",
+        "outputId": "16c6f794-d41f-4d7c-e0f0-d3b93fd1f688"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "247bc93b19724c2eae38b148ebd36faa",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/266 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "4c23fc80cfa945e3ab8fbc0cefeaa88a",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/67.9k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "f41d6117df6e486b8f94e23b21a956a5",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/97.8M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from transformers import AutoFeatureExtractor, AutoModelForImageClassification\n",
+        "\n",
+        "feature_extractor = AutoFeatureExtractor.from_pretrained(\"microsoft/resnet-50\")\n",
+        "model = AutoModelForImageClassification.from_pretrained(\"microsoft/resnet-50\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "u_uyzAMKp16S",
+        "outputId": "a6695a1c-b698-47d4-d3c0-d107106de358"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<class 'transformers.models.resnet.modeling_resnet.ResNetForImageClassification'>\n"
+          ]
+        }
+      ],
+      "source": [
+        "# automatically instantiates a ResNetForImageClassification\n",
+        "print(type(model))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "i3AFtDOYR__7"
+      },
+      "source": [
+        "## Pipeline API\n",
+        "\n",
+        "The [Pipeline API](https://huggingface.co/docs/transformers/main_classes/pipelines) allows you to just feed a text/image/audio to it, and get a prediction out. All complexity is handled by the pipeline.\n",
+        "\n",
+        "Currently supported:\n",
+        "* image classification (models include ViT, BEiT, DeiT, ConvNeXT, Swin Transformer, CvT, SegFormer, VAN, ResNet, RegNet, LeViT,...)\n",
+        "* object detection (models include DETR and YOLOS)\n",
+        "* image segmentation (models include DETR, SegFormer, DPT, BEiT)\n",
+        "\n",
+        "Soon:\n",
+        "* visual question answering (VQA)\n",
+        "* image-to-text\n",
+        "* depth estimation\n",
+        "\n",
+        "### Image classification pipeline\n",
+        "\n",
+        "Image classification is probably the simplest vision task: given an image, predict which class(es) belong to it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "X40QYLLwSBS4",
+        "outputId": "9329ab3b-6d1c-4409-8199-c2423d72138b"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "No model was supplied, defaulted to google/vit-base-patch16-224 (https://huggingface.co/google/vit-base-patch16-224)\n"
+          ]
+        }
+      ],
+      "source": [
+        "from transformers import pipeline\n",
+        "\n",
+        "image_pipe = pipeline(\"image-classification\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "X-JmQ3cbSLKD",
+        "outputId": "983564df-1607-4f24-bb40-424151d59364"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[{'label': 'Egyptian cat', 'score': 0.9374412894248962},\n",
+              " {'label': 'tabby, tabby cat', 'score': 0.03844260051846504},\n",
+              " {'label': 'tiger cat', 'score': 0.014411398209631443},\n",
+              " {'label': 'lynx, catamount', 'score': 0.003274323185905814},\n",
+              " {'label': 'Siamese cat, Siamese', 'score': 0.0006795922527089715}]"
+            ]
+          },
+          "execution_count": 27,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "image_pipe(image)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "K0gn_i-8xaqL"
+      },
+      "source": [
+        "Note that you can also provide a custom model from the hub:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 169,
+          "referenced_widgets": [
+            "29c30d0633534ac89d5059513ea85f92",
+            "6b916a7d0f544a63b80a3efe9dfd0f44",
+            "46995a901449477087bb712b3a0dfcf0",
+            "eef08a301ddc44e2b0f7b74db6f86127",
+            "73ca073b98994dcba03670961dbc49ae",
+            "40ef861ef9a94ac09e9858dfce45a644",
+            "6f73931aeaf1432ebb31882fdf83d462",
+            "c04e2c71bdd94b3eb8375fa75a134bfd",
+            "0729f9745cce428aadc5dfbdeadb9273",
+            "20ce43c9abef4017a9befe6472c5278e",
+            "f0728765a7df43a9bd021f1472254836",
+            "c31e28dd39e24325a1a303d0262ad0a0",
+            "d0d3c4d7409546909e3f8acf98785182",
+            "b03d4140889e429eb6a84a6518e46353",
+            "ffcc25068ca14b8f84380eea7f4f46d0",
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+            "2f2df35b25634b6e871ec11c250741d9",
+            "dcd5525b4c2141ab9a387ddf76b51c88",
+            "57cfa7f2d5c24955a4b29a9daf121aff",
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+            "5d0701fa229e440ab1af934ad360f948",
+            "d9d9c1e654ea4eb4bb3b1d8cb7b1c6ba"
+          ]
+        },
+        "id": "yOTEmXU2Pk3l",
+        "outputId": "bc81f8a5-75d2-4141-bfed-4de4cb207d3b"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "29c30d0633534ac89d5059513ea85f92",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/70.1k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "c31e28dd39e24325a1a303d0262ad0a0",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/108M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2228.)\n",
+            "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "811fbfbf52b5429e8559aa64ffddf374",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/255 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "image_pipe = pipeline(\"image-classification\", \n",
+        "                      model=\"microsoft/swin-tiny-patch4-window7-224\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Nw_gfKd-PnWw",
+        "outputId": "b6f1cce0-92bd-4987-de41-e8d61f46b17a"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[{'label': 'tabby, tabby cat', 'score': 0.3746732473373413},\n",
+              " {'label': 'tiger cat', 'score': 0.14187973737716675},\n",
+              " {'label': 'Egyptian cat', 'score': 0.11662525683641434},\n",
+              " {'label': 'remote control, remote', 'score': 0.05929144471883774},\n",
+              " {'label': 'crutch', 'score': 0.01797829568386078}]"
+            ]
+          },
+          "execution_count": 29,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "image_pipe(image)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5z7d3m0bPto8"
+      },
+      "source": [
+        "... or a model + feature extractor:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 113,
+          "referenced_widgets": [
+            "ba02948164d64f0b87796ac14c453267",
+            "ca4958dcc9114bf89ad6d44512eb36a0",
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+            "e59a5bc83b234152b0a146b2e49c4567",
+            "eb9206aa68ac4a788e97a87b2945321f",
+            "0ad2e088a86b43418e4bbb70135b8224",
+            "11b0c789aee548e3810643056ec0cd9b",
+            "a30386687d184797a16a8fd379d6acb0",
+            "7a350d8644ba47e490d72aff9106d8f3",
+            "c14332dc84ed49ddaf13af4694e290de",
+            "02584ce2d7db46eda58dd452a85deb97",
+            "44dfbdd5285f46de851d0e8bab99ed43",
+            "c7acbea892d84d459ee44ff168a80efe",
+            "0bfa226a7de042baaf4e6255cf6ed967",
+            "8a36f0a967c3494594b6f3d1256b853f",
+            "b6f4a6a646af4643a7d84a77cc344d71",
+            "9807ff10a451493ab9bc9818dedbedbd",
+            "f1a42a947069430f960f5276cdc9ffcb",
+            "19b0717774d744b39a3806a39925a9c0",
+            "889eb41f139a4fc0a9d59859874986b9",
+            "b7fa96636d3c4609bd0434257436285f",
+            "471e7c8f428044bf97a7ef360c1a599b",
+            "2bf17567a3f14dd7ac8e3450e5470656",
+            "51dc1b6f6d064d0cbf476175ea5b32b4",
+            "ab7bb54aecf248f3adb523b56f55af94",
+            "f7178833ab504e97b86ea0907704cdeb",
+            "e4a583b82cb049d29c134ae631c7f6c1",
+            "87f56812ab744f2db24c8c122033c740",
+            "4670544334d84043864442af4ea93612",
+            "02b3ac5b390b427eb4b1fbc849a52485",
+            "54258459323d4ca498f179b3c9dd2c3b",
+            "69e37e34c7524e1db831315aea772845",
+            "59c2e4d771ca420994f6dbcb200a77aa"
+          ]
+        },
+        "id": "Mxb90TPUxcRp",
+        "outputId": "19ec40a5-3f11-469a-ccb2-34637c2e6d17"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "ba02948164d64f0b87796ac14c453267",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/266 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "44dfbdd5285f46de851d0e8bab99ed43",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/68.0k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "2bf17567a3f14dd7ac8e3450e5470656",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/109M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# load a ConvNeXT model\n",
+        "feature_extractor = AutoFeatureExtractor.from_pretrained(\"facebook/convnext-tiny-224\")\n",
+        "model = AutoModelForImageClassification.from_pretrained(\"facebook/convnext-tiny-224\")\n",
+        "\n",
+        "image_pipe = pipeline(\"image-classification\", \n",
+        "                      model=model,\n",
+        "                      feature_extractor=feature_extractor)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3wWElQYh5Fej",
+        "outputId": "490d6536-03fb-47b8-c939-48cbad878c4c"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[{'label': 'tabby, tabby cat', 'score': 0.6132748126983643},\n",
+              " {'label': 'tiger cat', 'score': 0.2736639082431793},\n",
+              " {'label': 'Egyptian cat', 'score': 0.009575320407748222},\n",
+              " {'label': 'quilt, comforter, comfort, puff', 'score': 0.0017712308326736093},\n",
+              " {'label': 'pillow', 'score': 0.0017434777691960335}]"
+            ]
+          },
+          "execution_count": 31,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "image_pipe(image)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9jFMoeCvu-Fg"
+      },
+      "source": [
+        "### Object detection pipeline\n",
+        "\n",
+        "Currently, the library includes 2 Transformer-based object detection models: [DETR](https://huggingface.co/docs/transformers/model_doc/detr) and [YOLOS](https://huggingface.co/docs/transformers/model_doc/yolos).\n",
+        "\n",
+        "Check out the interactive inference widget: https://huggingface.co/facebook/detr-resnet-50"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 169,
+          "referenced_widgets": [
+            "340c029578984213b324a8cb3d9b53c3",
+            "44c365a6be494a5db758fb0f42d22540",
+            "9d7a74fc55b24144af74836de9d2b4c5",
+            "0fad23865e6248d09de3d5eb5ef113e4",
+            "19d10f4cf34a4b0b87e70110bf508242",
+            "c70aeada82c94cdabaa5b5d5d232df84",
+            "0f4fc6ef56be45e5b96ff090020a3a9b",
+            "001da0115c27458fb2364aa9c853a2ca",
+            "2d8f41ff3cd5479da2d2c1109f80b798",
+            "e0ef6b06b1254a8584dea133ec38c19b",
+            "ac231e4dfb574277ab53605e754c033e",
+            "28a996f44fc246a1bf062921072a4c23",
+            "e60e0e9469984ce0a2e8e05484a153d8",
+            "359feecbefd942f5b2370f37a510392f",
+            "be415f0c92434f95ae4a57ccae64cbe3",
+            "814668620de7415c8c8317dc67864d98",
+            "9ca5105629eb4937bd78182b370ff2df",
+            "18ee5f991ede4b16879f2c114eadbdb6",
+            "459e951f621147478be7296fada63a9f",
+            "9b75c743206744a9af7124cd7c0c8461",
+            "b8ed6406cd8548be90ce9ea430c27e46",
+            "43f5d259ff4e4620b77459f5ad5deb88",
+            "19e80dd68a28425e89ac7be593596f79",
+            "496723589bf34e439efd7efcf5bf84c5",
+            "ff1739ffefc44eec9944e15c9d024df6",
+            "ba6f2aaf20524d25a3b6ec5b55ab8e9d",
+            "64c26e0ff1b54da3af85c911b2e9efa8",
+            "853c8862218f44b8baa1e3f238e81eb2",
+            "e5ed2e3e17dd45fe8655db1d922d184d",
+            "bcd90750c6f74a1bb9b50aa177aafb47",
+            "cbc1ad73bfce4149b4830a07c41a24ff",
+            "0ace9e737d2a473bbc8fdca9dbc7ebbf",
+            "34b8c6f11f9545c78a0a9effc58bbcc9"
+          ]
+        },
+        "id": "_TWTF-AASOOx",
+        "outputId": "0d96611a-a3b0-42ea-b308-980199360150"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "No model was supplied, defaulted to facebook/detr-resnet-50 (https://huggingface.co/facebook/detr-resnet-50)\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "340c029578984213b324a8cb3d9b53c3",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/4.48k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "28a996f44fc246a1bf062921072a4c23",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/159M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Downloading: \"https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_a1_0-14fe96d1.pth\" to /root/.cache/torch/hub/checkpoints/resnet50_a1_0-14fe96d1.pth\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "19e80dd68a28425e89ac7be593596f79",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/274 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "object_detection_pipe = pipeline(\"object-detection\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "-8fXtSA5Sl7r",
+        "outputId": "c7a4e406-d8e4-4b02-ecea-2b9c09186284"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[{'box': {'xmax': 175, 'xmin': 40, 'ymax': 117, 'ymin': 70},\n",
+              "  'label': 'remote',\n",
+              "  'score': 0.9982201457023621},\n",
+              " {'box': {'xmax': 368, 'xmin': 333, 'ymax': 187, 'ymin': 72},\n",
+              "  'label': 'remote',\n",
+              "  'score': 0.9960021376609802},\n",
+              " {'box': {'xmax': 639, 'xmin': 0, 'ymax': 473, 'ymin': 1},\n",
+              "  'label': 'couch',\n",
+              "  'score': 0.9954745173454285},\n",
+              " {'box': {'xmax': 314, 'xmin': 13, 'ymax': 470, 'ymin': 52},\n",
+              "  'label': 'cat',\n",
+              "  'score': 0.9988006353378296},\n",
+              " {'box': {'xmax': 640, 'xmin': 345, 'ymax': 368, 'ymin': 23},\n",
+              "  'label': 'cat',\n",
+              "  'score': 0.9986783862113953}]"
+            ]
+          },
+          "execution_count": 33,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "results = object_detection_pipe(image)\n",
+        "results"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GtpeyW_q2xPl"
+      },
+      "source": [
+        "Let's visualize the results:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "metadata": {
+        "id": "nRRWpOEGtEyg"
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# colors for visualization\n",
+        "COLORS = [[0.000, 0.447, 0.741], [0.850, 0.325, 0.098], [0.929, 0.694, 0.125], [0.494, 0.184, 0.556], [0.466, 0.674, 0.188]]\n",
+        "\n",
+        "def plot_results(image, results):\n",
+        "    plt.figure(figsize=(16,10))\n",
+        "    plt.imshow(image)\n",
+        "    ax = plt.gca()\n",
+        "    colors = COLORS * 100\n",
+        "    for result, color in zip(results, colors):\n",
+        "        box = result['box']\n",
+        "        xmin, xmax, ymin, ymax = box['xmin'], box['xmax'], box['ymin'], box['ymax']\n",
+        "        label = result['label']\n",
+        "        prob = result['score']\n",
+        "        ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin,\n",
+        "                                   fill=False, color=color, linewidth=3))\n",
+        "        text = f'{label}: {prob:0.2f}'\n",
+        "        ax.text(xmin, ymin, text, fontsize=15,\n",
+        "                bbox=dict(facecolor='yellow', alpha=0.5))\n",
+        "    plt.axis('off')\n",
+        "    plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 585
+        },
+        "id": "PEGOkNqLtI0d",
+        "outputId": "8e19dc72-8f0e-4ae3-8e48-807578f0e8f5"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1152x720 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plot_results(image, results)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FknA1N9tw9ck"
+      },
+      "source": [
+        "### Image segmentation pipeline\n",
+        "\n",
+        "Let's try the image segmentation pipeline. By default, a DETR model is used."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 131,
+          "referenced_widgets": [
+            "02cd9041b0224fa3a4cf880f0c45985d",
+            "2e0e883a5ba64e4ebe7467ff66abb3ea",
+            "0d272f9b7a094c9ca736ad866a1bef5b",
+            "20bf04454cdc4641b52ea4be013b6c38",
+            "82e8fae251514300ab2c6db08091a940",
+            "260667ddbfcc4e45b84cce342154490c",
+            "647400c28b3a40bab64315bab8eb54e3",
+            "c172170b46d54b798aabb272d7aaef95",
+            "41fea4646ecc4436b0d4f914a61c8a0c",
+            "4b42c5c51e244e86a127bcaf990bd5fd",
+            "0a16a06f6bd24d5390b7cdaa91e8b97b",
+            "e3af9820e79640e881aa3ed4c1b066f6",
+            "ff07eea706904ca1b5e62009f466478b",
+            "602ddffe98c14572a0a194f69618fb1b",
+            "b1a8fd86ed8342ca8abab37fe1b5a4a6",
+            "a22b490f1ca74dd1a57912cd3bb3b39d",
+            "345f8852861d4e238b97e78b02eeb306",
+            "eda4959d91a34006adff8dd11e04a839",
+            "4b4439b5d1d14515b7ea1185823896e3",
+            "97bf303cbc5a4a51b2576a17d56fd064",
+            "2067a949788d41e49914425f88ccdcd9",
+            "2339866570a44d42bf11f10d187d4638",
+            "92ce7f001374492188aede199bbca204",
+            "59566f88f9fc481dbb25a916bf6f974e",
+            "229112b0e02d479c9eac0670b911a0db",
+            "c6caa5f1f1e943c9b0094c60c1af5223",
+            "5cd4e0e7bb6d4821997efe66ebd4cc16",
+            "82829d8d1bb840e2bb9cf9b35406bc9c",
+            "1ecbe9017f1347e6bb6dafc014f0b8bf",
+            "d9ebf557e3134131bdc4cf3771d1794c",
+            "0160242380a444888c7b91e2934ed4b6",
+            "7c56738718424c578bf95845fced08eb",
+            "0373ca2dbbfa4411a1bc45289969c4b3"
+          ]
+        },
+        "id": "i-jMGCJTw_ho",
+        "outputId": "00061c71-f0da-42fe-e446-9e07b71ed5e4"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "No model was supplied, defaulted to facebook/detr-resnet-50-panoptic (https://huggingface.co/facebook/detr-resnet-50-panoptic)\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "02cd9041b0224fa3a4cf880f0c45985d",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/11.3k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "e3af9820e79640e881aa3ed4c1b066f6",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/164M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "92ce7f001374492188aede199bbca204",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/273 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "segmentation_pipe = pipeline(\"image-segmentation\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 186,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "yED6T7DJxT1l",
+        "outputId": "d1e8ca54-525b-45bc-8833-6d1ff2129731"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[{'label': 'blanket',\n",
+              "  'mask': <PIL.Image.Image image mode=L size=640x480 at 0x7F582681E590>,\n",
+              "  'score': 0.9094282388687134},\n",
+              " {'label': 'cat',\n",
+              "  'mask': <PIL.Image.Image image mode=L size=640x480 at 0x7F582681E290>,\n",
+              "  'score': 0.9940965175628662},\n",
+              " {'label': 'remote',\n",
+              "  'mask': <PIL.Image.Image image mode=L size=640x480 at 0x7F5826809C90>,\n",
+              "  'score': 0.9986692667007446},\n",
+              " {'label': 'remote',\n",
+              "  'mask': <PIL.Image.Image image mode=L size=640x480 at 0x7F5826809790>,\n",
+              "  'score': 0.9994757771492004},\n",
+              " {'label': 'couch',\n",
+              "  'mask': <PIL.Image.Image image mode=L size=640x480 at 0x7F5826809410>,\n",
+              "  'score': 0.9722068309783936},\n",
+              " {'label': 'cat',\n",
+              "  'mask': <PIL.Image.Image image mode=L size=640x480 at 0x7F5826809150>,\n",
+              "  'score': 0.9994235038757324}]"
+            ]
+          },
+          "execution_count": 186,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "segmentation_pipe(image)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-r1ml1C7P_BD"
+      },
+      "source": [
+        "For visualization, we refer to [this notebook](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/DETR/DETR_panoptic_segmentation_minimal_example_(with_DetrFeatureExtractor).ipynb).\n",
+        "\n",
+        "Check also the [inference widget](https://huggingface.co/facebook/detr-resnet-50-panoptic).\n",
+        "\n",
+        "\n",
+        "See also [SegFormer](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/SegFormer/Segformer_inference_notebook.ipynb) and [MaskFormer]( https://github.com/NielsRogge/Transformers-Tutorials/blob/master/MaskFormer/maskformer_minimal_example(with_MaskFormerFeatureExtractor).ipynb)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IQxeZCidcEgi"
+      },
+      "source": [
+        "## Other tasks\n",
+        "\n",
+        "* depth estimation (DPT, GLPN): https://huggingface.co/spaces/nielsr/dpt-depth-estimation\n",
+        "* optical flow: https://huggingface.co/spaces/nielsr/perceiver-optical-flow\n",
+        "\n",
+        "For an overview, see: https://huggingface.co/tasks"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WKwBj48_bVPz"
+      },
+      "source": [
+        "## Datasets\n",
+        "\n",
+        "### Loading a dataset from the hub\n",
+        "\n",
+        "Loading any dataset from the hub is as easy as:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 37,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 201,
+          "referenced_widgets": [
+            "3186989e75ea42b49b6968fdad675f0e",
+            "98521f9c810547a8b2088b2029e988fe",
+            "ecb66501abb8458a9fe87d66df1e8b9b",
+            "944aa3af20944fe1ad6a64f24b0311f2",
+            "1923a6701d3142de91fb18b8389b877d",
+            "cec7671880a44682bd2d9328279933d4",
+            "3fb154228b8b4f0cbfd7241fa7402e2c",
+            "702476251304489bad9e4739b6e6494c",
+            "5ed70ac5d4a94edabe580ba4e73dadae",
+            "e51745af8c9e4f3b9f8736d0c0a596dc",
+            "e83b7231b5ab490a90fe8a7f8daf8f6b",
+            "899f3acf8a514a6ca4ac646b7aef9722",
+            "a869e071191843f1a79eb233527042db",
+            "2ee71187b6ae4c55b3a3a2ff808f4c1b",
+            "df0468854a0b456792e7182a8e8cb5a8",
+            "518a8d5e56db4000a40e5d9f4a8ca33b",
+            "55d6f63d6b4e4b52bd5d3e1fda443eba",
+            "a310413fe1ad4e23b15fccf91c9ab22c",
+            "bd7aa91b7d9d41a39ebecda9c8e563e9",
+            "15b1d1466ffd41e18d73728133b0d982",
+            "1099d908c27d407bb3852e854db86a3a",
+            "5cf3a92da0ec4b02b7e9179e1c3d74ae",
+            "9a28ecd289254757a8921be09b647a56",
+            "319044a8127e498ea888271dc220d25d",
+            "8f58e4207b8c4f1b86e5aa45a7af1ae9",
+            "51702ed6311a40a1a80b016333c8742f",
+            "7f9b1792c5554e7cae829fef946442c1",
+            "4999b7c9f302485fb1e6c190af2170d6",
+            "123b8ffbf8a4478eb4922c83fa651d94",
+            "295122664d7443639e5b80d5a5583084",
+            "fe794c67ad5f443188910984832cce77",
+            "b1e78c9ac68c4bfcb40e133dbd569423",
+            "8d2e9e35185d4e0f9dc33f15e90162c0",
+            "da2fb6423abd44d29172a18334df6c88",
+            "79ea37ef27bb4c54b54d5f1e93909eb4",
+            "c1d17ec02e4b450e9da55afaada13afc",
+            "d06f377e53a44e4491a9d5b27daa79ad",
+            "5c3aa4e8316b4baa99ad373a03da36ad",
+            "0e0c22389bbe44eb8dc08bde90aa8f76",
+            "bb9e6c8e4951437caa08319315601dff",
+            "b7143ec6bf9b4704b2766ad671063fda",
+            "c7dc6d479cf948d1b4afa1673718cd3c",
+            "8cd5cbee19674579b9048774b93efe01",
+            "eca0e385777849e0bf854224c52c91fc",
+            "e1d2ddf731094c7198cfeca992d39661",
+            "fc8c8bc86d42465f811316d9336a4ff6",
+            "013b08183b4745c589fcdb58526f5ec5",
+            "6bcc8e8b7daf458f81186182f35de964",
+            "3371a438bf5b412ab5c9b7230fe27623",
+            "43e140f9909b4bda821f6e360678c370",
+            "86488d305931487bb0f986d4222fb381",
+            "9f539ba3e4bb4db08d16c87baf59835f",
+            "588af91d2c5942449cb3c2c11fe8acb6",
+            "fe429a84adc84180be600727fdce1524",
+            "324a6401e529466e9b0a97c7449c1d99",
+            "ee9207dcade74cd396713e492c898fb1",
+            "5c056c2760064927a02f7eac60b4fbc5",
+            "6229db0835a741b8b88a54308fef276b",
+            "67b362e258e1412b8a0d955911b684a6",
+            "c8281d1792784212a154c5259e4a085a",
+            "9114cc7c6ec444b9816c37ac80567309",
+            "53b4511119aa44ecaa25de712332d0e6",
+            "98c61aa306aa486fa2fdc74cc2637521",
+            "8c713e29a78048888bc694bc7a247ee1",
+            "3548d003c9b741bc8c50c60bfcb22acb",
+            "fc0d151b52b741f8a64ffbc19dfe5333"
+          ]
+        },
+        "id": "T5_9ImAVbG3G",
+        "outputId": "eb53af0e-fa28-4897-9696-c2096b391c0e"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "3186989e75ea42b49b6968fdad675f0e",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading builder script:   0%|          | 0.00/2.19k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "899f3acf8a514a6ca4ac646b7aef9722",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading metadata:   0%|          | 0.00/1.44k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Downloading and preparing dataset cifar100/cifar100 (download: 161.17 MiB, generated: 129.09 MiB, post-processed: Unknown size, total: 290.26 MiB) to /root/.cache/huggingface/datasets/cifar100/cifar100/1.0.0/f365c8b725c23e8f0f8d725c3641234d9331cd2f62919d1381d1baa5b3ba3142...\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "9a28ecd289254757a8921be09b647a56",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading data:   0%|          | 0.00/169M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "da2fb6423abd44d29172a18334df6c88",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating train split:   0%|          | 0/50000 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "e1d2ddf731094c7198cfeca992d39661",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating test split:   0%|          | 0/10000 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Dataset cifar100 downloaded and prepared to /root/.cache/huggingface/datasets/cifar100/cifar100/1.0.0/f365c8b725c23e8f0f8d725c3641234d9331cd2f62919d1381d1baa5b3ba3142. Subsequent calls will reuse this data.\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "ee9207dcade74cd396713e492c898fb1",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "  0%|          | 0/2 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from datasets import load_dataset \n",
+        "\n",
+        "dataset = load_dataset(\"cifar100\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MrV8Jc6FWfUd"
+      },
+      "source": [
+        "There is support for many known vision datasets, like MNIST, CIFAR10/100, FashionMNIST, Visual Genome, PascalVOC, and now also... ImageNet!\n",
+        "\n",
+        "ImageNet is now available on the hub! https://huggingface.co/datasets/imagenet-1k\n",
+        "\n",
+        "More and more coming (soon: COCO).\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 39,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Z4tAuxTwfrme",
+        "outputId": "52e1e146-4c77-4473-addd-de093b92bdec"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Dataset({\n",
+              "    features: ['img', 'fine_label', 'coarse_label'],\n",
+              "    num_rows: 50000\n",
+              "})"
+            ]
+          },
+          "execution_count": 39,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "dataset['train']"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bEV807EdfqN0"
+      },
+      "source": [
+        "### Image feature\n",
+        "\n",
+        "You can directly view images in a notebook, as the images are of type [Image](https://huggingface.co/docs/datasets/v2.2.1/en/package_reference/main_classes#datasets.Image). Awesome, isn't it?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 42,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 49
+        },
+        "id": "NAH_qeMIfsfP",
+        "outputId": "aa1e5eed-5046-4669-bfc9-c040eb05f335"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": "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",
+            "text/plain": [
+              "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32 at 0x7F79A0CC9A90>"
+            ]
+          },
+          "execution_count": 42,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "dataset['train'][0]['img']"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 43,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dY2AQl_GfwYE",
+        "outputId": "93d536e7-17e8-489e-82fa-6118a38d8e18"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "19"
+            ]
+          },
+          "execution_count": 43,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "dataset['train'][0]['fine_label']"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 44,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "di7PMENA0yN3",
+        "outputId": "e560f509-417c-4c97-d230-2dfabf8b10e8"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "{'coarse_label': ClassLabel(num_classes=20, names=['aquatic_mammals', 'fish', 'flowers', 'food_containers', 'fruit_and_vegetables', 'household_electrical_devices', 'household_furniture', 'insects', 'large_carnivores', 'large_man-made_outdoor_things', 'large_natural_outdoor_scenes', 'large_omnivores_and_herbivores', 'medium_mammals', 'non-insect_invertebrates', 'people', 'reptiles', 'small_mammals', 'trees', 'vehicles_1', 'vehicles_2'], id=None),\n",
+              " 'fine_label': ClassLabel(num_classes=100, names=['apple', 'aquarium_fish', 'baby', 'bear', 'beaver', 'bed', 'bee', 'beetle', 'bicycle', 'bottle', 'bowl', 'boy', 'bridge', 'bus', 'butterfly', 'camel', 'can', 'castle', 'caterpillar', 'cattle', 'chair', 'chimpanzee', 'clock', 'cloud', 'cockroach', 'couch', 'cra', 'crocodile', 'cup', 'dinosaur', 'dolphin', 'elephant', 'flatfish', 'forest', 'fox', 'girl', 'hamster', 'house', 'kangaroo', 'keyboard', 'lamp', 'lawn_mower', 'leopard', 'lion', 'lizard', 'lobster', 'man', 'maple_tree', 'motorcycle', 'mountain', 'mouse', 'mushroom', 'oak_tree', 'orange', 'orchid', 'otter', 'palm_tree', 'pear', 'pickup_truck', 'pine_tree', 'plain', 'plate', 'poppy', 'porcupine', 'possum', 'rabbit', 'raccoon', 'ray', 'road', 'rocket', 'rose', 'sea', 'seal', 'shark', 'shrew', 'skunk', 'skyscraper', 'snail', 'snake', 'spider', 'squirrel', 'streetcar', 'sunflower', 'sweet_pepper', 'table', 'tank', 'telephone', 'television', 'tiger', 'tractor', 'train', 'trout', 'tulip', 'turtle', 'wardrobe', 'whale', 'willow_tree', 'wolf', 'woman', 'worm'], id=None),\n",
+              " 'img': Image(decode=True, id=None)}"
+            ]
+          },
+          "execution_count": 44,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "dataset['train'].features"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 45,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Oha_czj9f0Ip",
+        "outputId": "be55f67d-4a4b-4027-f217-1c05b305dffd"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{0: 'apple', 1: 'aquarium_fish', 2: 'baby', 3: 'bear', 4: 'beaver', 5: 'bed', 6: 'bee', 7: 'beetle', 8: 'bicycle', 9: 'bottle', 10: 'bowl', 11: 'boy', 12: 'bridge', 13: 'bus', 14: 'butterfly', 15: 'camel', 16: 'can', 17: 'castle', 18: 'caterpillar', 19: 'cattle', 20: 'chair', 21: 'chimpanzee', 22: 'clock', 23: 'cloud', 24: 'cockroach', 25: 'couch', 26: 'cra', 27: 'crocodile', 28: 'cup', 29: 'dinosaur', 30: 'dolphin', 31: 'elephant', 32: 'flatfish', 33: 'forest', 34: 'fox', 35: 'girl', 36: 'hamster', 37: 'house', 38: 'kangaroo', 39: 'keyboard', 40: 'lamp', 41: 'lawn_mower', 42: 'leopard', 43: 'lion', 44: 'lizard', 45: 'lobster', 46: 'man', 47: 'maple_tree', 48: 'motorcycle', 49: 'mountain', 50: 'mouse', 51: 'mushroom', 52: 'oak_tree', 53: 'orange', 54: 'orchid', 55: 'otter', 56: 'palm_tree', 57: 'pear', 58: 'pickup_truck', 59: 'pine_tree', 60: 'plain', 61: 'plate', 62: 'poppy', 63: 'porcupine', 64: 'possum', 65: 'rabbit', 66: 'raccoon', 67: 'ray', 68: 'road', 69: 'rocket', 70: 'rose', 71: 'sea', 72: 'seal', 73: 'shark', 74: 'shrew', 75: 'skunk', 76: 'skyscraper', 77: 'snail', 78: 'snake', 79: 'spider', 80: 'squirrel', 81: 'streetcar', 82: 'sunflower', 83: 'sweet_pepper', 84: 'table', 85: 'tank', 86: 'telephone', 87: 'television', 88: 'tiger', 89: 'tractor', 90: 'train', 91: 'trout', 92: 'tulip', 93: 'turtle', 94: 'wardrobe', 95: 'whale', 96: 'willow_tree', 97: 'wolf', 98: 'woman', 99: 'worm'}\n"
+          ]
+        }
+      ],
+      "source": [
+        "id2label = {id: label for id, label in enumerate(dataset['train'].features['fine_label'].names)}\n",
+        "print(id2label)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 46,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "id": "KRXUJDWpgFcG",
+        "outputId": "9ac1ba1b-8e5f-4745-f545-1766c035692b"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            },
+            "text/plain": [
+              "'cattle'"
+            ]
+          },
+          "execution_count": 46,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "id2label[19]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WWnByIwQd4N2"
+      },
+      "source": [
+        "### Loading a dataset from local/remote files: ImageFolder\n",
+        "\n",
+        "The [ImageFolder](https://huggingface.co/docs/datasets/v2.0.0/en/image_process#imagefolder) builder is very handy to create a Dataset from local or remote files/folders.\n",
+        "\n",
+        "It will automatically create a dataset with 2 columns, \"image\" and \"label\" (based on directory structure).\n",
+        "\n",
+        "Let's say you have a remote tgz file: https://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html?ref=hackernoon.com"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 47,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 251,
+          "referenced_widgets": [
+            "eee3f3ba19794c7a961251ec1500af4f",
+            "4ea1ef0070b4488d89ca58f1b49a0a1d",
+            "a98654eef27848999db523b11dc629ef",
+            "64943c92c083440b8fc6ef34d176f3cd",
+            "5ad23b0d9fc841c78f9fdd82a583fcf0",
+            "e7320ec976ab468b9dfbe92e72e405cf",
+            "23f23c71ff3142a19fd66330eadf1d8c",
+            "298380d2ca6d4ca5a221c3036e546fac",
+            "c3d7196996bf4fc783715921bd57b514",
+            "2efac48bf966407dbbdd6185b746acd3",
+            "38876295726f47f1abffb8bfb28242f0",
+            "fa4d2548d09e4dc2bcd3419acb94cd96",
+            "cb619ae1c8cc4b53b65eab78a2949d88",
+            "dc6c190bb7b64b27887fab5f32def509",
+            "8362e331c95f4797aed82468acba0591",
+            "e94bde66c9f649ee85f273a271d66853",
+            "a1aecb72c2c84aa7ae629a725cbe1c7c",
+            "4483b6cf1dd348b9af2d99dfd44440cd",
+            "c9e136be9e27403190f97621bf0c2360",
+            "4368af454a524f2693415c016a4800be",
+            "1024dc2d2b154759b8109f6a897a2fee",
+            "41a5174bc2c34ca38608c6e9629290e3",
+            "ebece6ce2a704036925fa3cfdfe07bc3",
+            "85d9db12932f405c859ee7ed934dd597",
+            "1ab6ccdd683b45afb861b002559331fb",
+            "07d918a32bc74fc88322dc4872c99553",
+            "32a5290505a84c0bb601f5919d0ddcc3",
+            "7d5222c25b7a4d46a38c0612924c56fd",
+            "2d28fd9efb064a6da8a2b6eba7754c49",
+            "011c4e736ff249dba90d5cd1a1606961",
+            "5fdf7d5836e040c382b28c7f2b626e86",
+            "daa2f4f8369740608246f483c9f41bf0",
+            "9a982e7499574ee7ac372653236ac017",
+            "55605d08c1944709ae8d26931c8be8f5",
+            "403aa2d1656248eaaead105c5404afee",
+            "d8ce5ff0f10c4c3eb344e5f93978b33d",
+            "488bf505e7ce430eba7a031278e84d65",
+            "29caa1a9eaaa46289907ed1d8afa6e18",
+            "656846d428c14f06955f6f96864a9465",
+            "c503e40b90064b98b9dc7aa439d6c5a0",
+            "e79898e9020844cca1928f9136562320",
+            "5c1d822500534adbbcdded6b3f2c8d11",
+            "35d8e0ecb38d40f38dbfc749d2b0a9a1",
+            "c6cad6f551a8418593bd2b59eed85bfa",
+            "855f66f34dd14ad2815f3c6572b38d44",
+            "3bb28cba70eb41e7a187834eff9ae53a",
+            "dc29b44fe051481798b2864c4bfc64b7",
+            "32e03b9289084b7c8a1767b88f4589ed",
+            "39e30189361e47ada33eb9ae5cc3d0c5",
+            "698b0e78c2004df4a124a3154fac177d",
+            "09f88588cec84029ad66b9d2f4d30b70",
+            "6823ab3fcf8649fdbdbc74945cf7609a",
+            "8b2e6f860b3a4389a6927bce42ae0cc3",
+            "78ef4b5e93c1464d8575e2f73e8b11f9",
+            "1dae25a4d6154128bb80e1fe0fce59a0",
+            "6a0f7103b8b741eebdf32d28189cc124",
+            "689ed43f2e2d45b49b8d49b0681a9b17",
+            "4ba60026d8f84e63962de7a0bcff68ac",
+            "9dd6a8362a53483cbe6f638e53ea548a",
+            "585218ce6f16419c80dfb0484514c1a1",
+            "dbcf62a4eee242439357cb17f8e755db",
+            "7b5e2a733e2d40de9882c598b17e2f96",
+            "b2988973aecd46518f686e5d3f2a6d80",
+            "1786794d8baa47df8c5f8c3f1b9222db",
+            "7a7b3baca14548aea00d68d1e239f9c7",
+            "c1cc358324ef43c89c3d24e44e180754"
+          ]
+        },
+        "id": "g9TN9KJId3tG",
+        "outputId": "ad0dd3cd-c2be-4e02-a340-e6c0fadc7080"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Using custom data configuration default-3b023c015cb58e5d\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Downloading and preparing dataset image_folder/default to /root/.cache/huggingface/datasets/image_folder/default-3b023c015cb58e5d/0.0.0/48efdc62d40223daee675ca093d163bcb6cb0b7d7f93eb25aebf5edca72dc597...\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "eee3f3ba19794c7a961251ec1500af4f",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading data files: 0it [00:00, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "fa4d2548d09e4dc2bcd3419acb94cd96",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "ebece6ce2a704036925fa3cfdfe07bc3",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading data:   0%|          | 0.00/345M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "55605d08c1944709ae8d26931c8be8f5",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "855f66f34dd14ad2815f3c6572b38d44",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating train split: 0 examples [00:00, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Dataset image_folder downloaded and prepared to /root/.cache/huggingface/datasets/image_folder/default-3b023c015cb58e5d/0.0.0/48efdc62d40223daee675ca093d163bcb6cb0b7d7f93eb25aebf5edca72dc597. Subsequent calls will reuse this data.\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "6a0f7103b8b741eebdf32d28189cc124",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "  0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from datasets import load_dataset\n",
+        "\n",
+        "dataset = load_dataset(\"imagefolder\", data_files=\"https://www.robots.ox.ac.uk/~vgg/data/flowers/102/102flowers.tgz\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 48,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 570
+        },
+        "id": "S1NPS3tCeQXQ",
+        "outputId": "f901a784-893d-493a-9888-d5a9beccab67"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x553 at 0x7F79A0070E90>"
+            ]
+          },
+          "execution_count": 48,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "example = dataset['train'][128]\n",
+        "example['image']"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Zyca-8bH9rso"
+      },
+      "source": [
+        "Similar to models, pushing (and reloading) to the hub is very easy:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 49,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 99,
+          "referenced_widgets": [
+            "f49cbf34b02c48398eb8784c93a4c661",
+            "ab0e57d6b33c4c10b5c8067d1cc9b7ab",
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+        },
+        "id": "L6EkZQ6YpUPA",
+        "outputId": "4d66ffa8-8b67-4d5b-8a01-312a652102f7"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Pushing split train to the Hub.\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "f49cbf34b02c48398eb8784c93a4c661",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Pushing dataset shards to the dataset hub:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "df53601719bd448aa0d0ae19fd1d5fe2",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "  0%|          | 0/9 [00:00<?, ?ba/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "dataset.push_to_hub(\"nielsr/flowers-demo\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KAL_htg5W3s8"
+      },
+      "source": [
+        "To prepare an image dataset for a model (such as performing data augmentation, creating PyTorch dataloaders etc.), refer to the [example scripts](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification) and example notebooks ([torchvision](https://github.com/huggingface/notebooks/blob/main/examples/image_classification.ipynb), [Albumentations](https://github.com/huggingface/notebooks/blob/main/examples/image_classification_albumentations.ipynb))."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kGlIE3m_T-PA"
+      },
+      "source": [
+        "## Extra: multimodal models\n",
+        "\n",
+        "Multi-modal models combine several modalities (e.g. language, vision, audio,...)\n",
+        "* AI models are getting more powerful due to this! Humans also capture several modalities at the same time.\n",
+        "* in Transformer, a so-called `Processor` can be used to prepare the inputs for a model. Internally, a processor combines a tokenizer (for the text modality) and a  feature extractor (for the image/audio modality) for a model.\n",
+        "\n",
+        "### Visual question answering (VQA)\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 50,
+      "metadata": {
+        "colab": {
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+        "id": "ObAR5_jjU5q3",
+        "outputId": "ae36625f-3259-47f0-b69a-791fdd78a43d"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "ef06858634594efcb19cbba7110ae09b",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/251 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "f8d268b204694f75b8defd049c4dc9cd",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/320 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "f55772bc74ea47649c3b6d289f76daeb",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/226k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "e90c71b97be2438abb578240387d7182",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/455k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "66664081b3e24c8fbd48aabd2c233040",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/112 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "1d825810b1994bbc84f05b5510bf4a94",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/132k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "c7862ae42cac4dfb9b9087958239716b",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/449M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from transformers import ViltProcessor, ViltForQuestionAnswering\n",
+        "\n",
+        "processor = ViltProcessor.from_pretrained(\"dandelin/vilt-b32-finetuned-vqa\")\n",
+        "model = ViltForQuestionAnswering.from_pretrained(\"dandelin/vilt-b32-finetuned-vqa\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 52,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "eLO-3iOzVDfY",
+        "outputId": "d014cd82-cde8-457d-f5e7-29a36777b0e8"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values', 'pixel_mask'])\n"
+          ]
+        }
+      ],
+      "source": [
+        "question = \"how many cats are there?\"\n",
+        "\n",
+        "encoding = processor(image, question, return_tensors=\"pt\")\n",
+        "print(encoding.keys()) "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 53,
+      "metadata": {
+        "id": "5-4UCI_6VPiR"
+      },
+      "outputs": [],
+      "source": [
+        "# forward pass\n",
+        "outputs = model(**encoding)\n",
+        "logits = outputs.logits"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 54,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4yAPZK_AVS8J",
+        "outputId": "564d6ba6-1eea-408d-880f-c9e8e39353cb"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Predicted answer: 2\n"
+          ]
+        }
+      ],
+      "source": [
+        "predicted_class_idx = logits.argmax(-1).item()\n",
+        "print(\"Predicted answer:\", model.config.id2label[predicted_class_idx])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "L-Mo_UiQU3Um"
+      },
+      "source": [
+        "### Document AI\n",
+        "\n",
+        "We also do provide several models that are useful for understanding scanned documents (like PDFs).\n",
+        "\n",
+        "* LayoutLM family (form understanding, DocVQA, document image classification): https://huggingface.co/spaces/nielsr/LayoutLMv2-FUNSD. Latest model is [LayoutLMv3](https://huggingface.co/docs/transformers/main/en/model_doc/layoutlmv3)\n",
+        "* [DiT](https://huggingface.co/docs/transformers/main/en/model_doc/dit) (Document Image Transformer): layout analysis: https://huggingface.co/spaces/nielsr/dit-document-layout-analysis \n",
+        "* [TrOCR](https://huggingface.co/docs/transformers/main/en/model_doc/trocr) (optical character recognition): https://huggingface.co/spaces/nielsr/TrOCR-handwritten"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UYI-VKxmWMaq"
+      },
+      "source": [
+        "### CLIP\n",
+        "\n",
+        "[CLIP](https://huggingface.co/docs/transformers/main/en/model_doc/clip) is a model by OpenAI that was trained on 400 million (image, text) pairs in a constrastive way. The model consists of 2 encoders (an image and a text encoder), that can be used to match image with text.\n",
+        "\n",
+        "The model is also capable of zero-shot image classification."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 55,
+      "metadata": {
+        "colab": {
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+            "d28ed1152fe7423c9297acc3b4666cfd",
+            "6c24198121c9477d830b218e848920df",
+            "59c12ac23e3c41ac912f2115b5a879b3",
+            "7bb7d38bdc024b0995a334171f0a9ea4",
+            "2c40c6c149ff4187a6d9faca50242ec7",
+            "25fa1664569c4a4383e53eced19c16a9",
+            "043f0589c8034df495f5a0614c2ef893",
+            "2f37b1b2e57e42bdac471feb61e5ded9",
+            "5994d7d67f6b4678ac354e1cc1800cc2",
+            "e754aa1e6885498ea098ee46b03ee3f5",
+            "5d63be1c95324036a62ca0f2b9ebc8f6",
+            "ae22bb62fa5c4f12a2eaa98350d77fbf",
+            "d7335a96b8cb4925837593ffc69c6afc",
+            "3a49c633cf4a4d1e89abfa3569603dde",
+            "62d8a873a6a64f7c9dec12e4c0c60b1d",
+            "05e4c57b8d234c22849d75e7f4bd8318",
+            "cfa096546b2049c0975ea77d86380ac8",
+            "96fab1b1dc404519a276ef466cc6b114",
+            "20301bc5fd154013aa828735dc966a9c"
+          ]
+        },
+        "id": "i75w53BzWPGM",
+        "outputId": "3c3d5d96-1d5f-4bff-d2ea-b665e8223cbf"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "d4410af262ff4e83b39eae65042059db",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/4.03k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "3487724515434d12bc06aa8d1ad998cc",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/577M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "1267a9fd1e354e68a3e918d8287efcde",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/316 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "bb04b8c68c0845afae9f939bc13c50b1",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/568 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "600cc970653c4b8a932887cb6c683bb4",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/842k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "6fbc5e09b2d04c96bb9b65e8c17cd368",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/512k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "4d7f57f043d740f886e9b1bbb9d115f9",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/2.12M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "5994d7d67f6b4678ac354e1cc1800cc2",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/389 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from transformers import CLIPProcessor, CLIPModel\n",
+        "\n",
+        "model = CLIPModel.from_pretrained(\"openai/clip-vit-base-patch32\")\n",
+        "processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-base-patch32\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 57,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 497
+        },
+        "id": "nuTb0xBj3Trg",
+        "outputId": "d6f8e64c-14cd-4dd2-a11c-48a85fd48963"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480 at 0x7F79AB709450>"
+            ]
+          },
+          "execution_count": 57,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "image"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 58,
+      "metadata": {
+        "id": "kE7wJAQIWWUS"
+      },
+      "outputs": [],
+      "source": [
+        "inputs = processor(text=[\"two cats sitting on a couch\", \"one cat sitting on a couch\"], images=image, return_tensors=\"pt\", padding=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 59,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "v7YIL_twWX_t",
+        "outputId": "d8595960-9c82-46c1-8ec4-ecd789806fec"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "tensor([[0.9762, 0.0238]], grad_fn=<SoftmaxBackward0>)\n"
+          ]
+        }
+      ],
+      "source": [
+        "outputs = model(**inputs)\n",
+        "logits_per_image = outputs.logits_per_image  # this is the image-text similarity score\n",
+        "probs = logits_per_image.softmax(dim=1)  # we can take the softmax to get the label probabilities\n",
+        "print(probs)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hJWM08NzR0r6"
+      },
+      "source": [
+        "We do provide all of this in a \"zero-shot image classification\" pipeline:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 60,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dNNaWUAQR31c",
+        "outputId": "0d84343d-b39b-4c64-9003-10c20378f2c6"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "No model was supplied, defaulted to openai/clip-vit-base-patch32 (https://huggingface.co/openai/clip-vit-base-patch32)\n"
+          ]
+        }
+      ],
+      "source": [
+        "zero_shot = pipeline(\"zero-shot-image-classification\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 61,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lBI18sxaR_Xa",
+        "outputId": "00f44f4f-e4b6-4e55-c183-3b9d51dd4aab"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[{'label': 'two cats sitting on a couch', 'score': 0.9810003042221069},\n",
+              " {'label': 'one cat sitting on a couch', 'score': 0.018999747931957245}]"
+            ]
+          },
+          "execution_count": 61,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "zero_shot(images=image, candidate_labels=[\"two cats sitting on a couch\", \"one cat sitting on a couch\"])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kt_vxLSAUUeJ"
+      },
+      "source": [
+        "### VisionEncoderDecoderModel\n",
+        "\n",
+        "* allows to use any Transformer-based vision encoder (e.g. ViT, Swin, BEiT) with any language decoder (e.g. BERT, GPT-2)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 62,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 254,
+          "referenced_widgets": [
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+            "e64fa0fd9b85494982587c44f6a324af",
+            "4abb4222d24d447483b90fae3ddce1b2",
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+            "7ade8963522340538bdd8c2640ad7a13",
+            "4e50f8e74e4e4e44a57defb935f126ff",
+            "79910c67511042d6b3d2b7c64c39025a",
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+            "78d580a17946424b9dcb7b87b11a0848",
+            "2739bb3935504557b9202c18e3ac9737",
+            "ce9f65c5902c4358ab00482235a53ee3",
+            "0e4ef50482354401bd20e8ba366ff1bf"
+          ]
+        },
+        "id": "4reO1CGeUO4k",
+        "outputId": "ff1bf2ec-62a8-4d79-ddac-7ca90f2938d4"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "d26381d5d1334bf19d8ceead8faf7927",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/502 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "8996ee989cf24377adc1158fce6b1efc",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/330M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "7269fc577bf944aa84195b1bfba6a079",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/570 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "4e50f8e74e4e4e44a57defb935f126ff",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/420M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertLMHeadModel: ['cls.seq_relationship.weight', 'cls.seq_relationship.bias']\n",
+            "- This IS expected if you are initializing BertLMHeadModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+            "- This IS NOT expected if you are initializing BertLMHeadModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
+            "Some weights of BertLMHeadModel were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['bert.encoder.layer.1.crossattention.self.key.weight', 'bert.encoder.layer.11.crossattention.self.query.bias', 'bert.encoder.layer.3.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.0.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.7.crossattention.output.dense.bias', 'bert.encoder.layer.4.crossattention.self.key.weight', 'bert.encoder.layer.4.crossattention.output.dense.bias', 'bert.encoder.layer.8.crossattention.output.dense.weight', 'bert.encoder.layer.9.crossattention.self.value.bias', 'bert.encoder.layer.4.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.8.crossattention.output.dense.bias', 'bert.encoder.layer.1.crossattention.self.value.bias', 'bert.encoder.layer.10.crossattention.self.key.weight', 'bert.encoder.layer.2.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.10.crossattention.self.value.weight', 'bert.encoder.layer.11.crossattention.self.query.weight', 'bert.encoder.layer.9.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.11.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.10.crossattention.self.key.bias', 'bert.encoder.layer.2.crossattention.self.key.weight', 'bert.encoder.layer.8.crossattention.self.query.weight', 'bert.encoder.layer.8.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.5.crossattention.output.dense.weight', 'bert.encoder.layer.5.crossattention.self.query.weight', 'bert.encoder.layer.10.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.10.crossattention.output.dense.weight', 'bert.encoder.layer.6.crossattention.output.dense.bias', 'bert.encoder.layer.3.crossattention.output.dense.weight', 'bert.encoder.layer.3.crossattention.self.key.bias', 'bert.encoder.layer.3.crossattention.self.value.weight', 'bert.encoder.layer.9.crossattention.self.query.bias', 'bert.encoder.layer.9.crossattention.self.value.weight', 'bert.encoder.layer.0.crossattention.self.value.weight', 'bert.encoder.layer.9.crossattention.self.key.weight', 'bert.encoder.layer.6.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.10.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.2.crossattention.self.query.bias', 'bert.encoder.layer.9.crossattention.self.query.weight', 'bert.encoder.layer.1.crossattention.self.query.bias', 'bert.encoder.layer.11.crossattention.self.key.weight', 'bert.encoder.layer.4.crossattention.output.dense.weight', 'bert.encoder.layer.11.crossattention.self.value.bias', 'bert.encoder.layer.10.crossattention.output.dense.bias', 'bert.encoder.layer.5.crossattention.self.key.bias', 'bert.encoder.layer.7.crossattention.self.value.weight', 'bert.encoder.layer.5.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.1.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.1.crossattention.self.value.weight', 'bert.encoder.layer.7.crossattention.self.query.bias', 'bert.encoder.layer.3.crossattention.self.query.bias', 'bert.encoder.layer.9.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.0.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.2.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.5.crossattention.self.value.bias', 'bert.encoder.layer.11.crossattention.output.dense.weight', 'bert.encoder.layer.5.crossattention.self.value.weight', 'bert.encoder.layer.3.crossattention.self.value.bias', 'bert.encoder.layer.6.crossattention.self.query.bias', 'bert.encoder.layer.0.crossattention.output.dense.bias', 'bert.encoder.layer.7.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.7.crossattention.self.key.weight', 'bert.encoder.layer.1.crossattention.self.key.bias', 'bert.encoder.layer.11.crossattention.self.value.weight', 'bert.encoder.layer.2.crossattention.self.key.bias', 'bert.encoder.layer.6.crossattention.self.value.bias', 'bert.encoder.layer.4.crossattention.self.query.weight', 'bert.encoder.layer.9.crossattention.output.dense.bias', 'bert.encoder.layer.11.crossattention.self.key.bias', 'bert.encoder.layer.6.crossattention.self.value.weight', 'bert.encoder.layer.2.crossattention.self.value.weight', 'bert.encoder.layer.6.crossattention.self.key.bias', 'bert.encoder.layer.7.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.7.crossattention.self.value.bias', 'bert.encoder.layer.1.crossattention.output.dense.weight', 'bert.encoder.layer.7.crossattention.self.query.weight', 'bert.encoder.layer.8.crossattention.self.value.bias', 'bert.encoder.layer.0.crossattention.self.query.bias', 'bert.encoder.layer.10.crossattention.self.query.weight', 'bert.encoder.layer.7.crossattention.self.key.bias', 'bert.encoder.layer.2.crossattention.output.dense.weight', 'bert.encoder.layer.11.crossattention.output.dense.bias', 'bert.encoder.layer.0.crossattention.output.dense.weight', 'bert.encoder.layer.6.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.8.crossattention.self.value.weight', 'bert.encoder.layer.9.crossattention.self.key.bias', 'bert.encoder.layer.2.crossattention.self.value.bias', 'bert.encoder.layer.2.crossattention.output.dense.bias', 'bert.encoder.layer.1.crossattention.self.query.weight', 'bert.encoder.layer.4.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.8.crossattention.self.query.bias', 'bert.encoder.layer.6.crossattention.output.dense.weight', 'bert.encoder.layer.2.crossattention.self.query.weight', 'bert.encoder.layer.8.crossattention.self.key.weight', 'bert.encoder.layer.6.crossattention.self.key.weight', 'bert.encoder.layer.8.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.0.crossattention.self.key.weight', 'bert.encoder.layer.3.crossattention.self.key.weight', 'bert.encoder.layer.0.crossattention.self.value.bias', 'bert.encoder.layer.4.crossattention.self.query.bias', 'bert.encoder.layer.10.crossattention.self.value.bias', 'bert.encoder.layer.5.crossattention.self.key.weight', 'bert.encoder.layer.5.crossattention.self.query.bias', 'bert.encoder.layer.7.crossattention.output.dense.weight', 'bert.encoder.layer.4.crossattention.self.value.weight', 'bert.encoder.layer.8.crossattention.self.key.bias', 'bert.encoder.layer.4.crossattention.self.key.bias', 'bert.encoder.layer.3.crossattention.self.query.weight', 'bert.encoder.layer.11.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.5.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.5.crossattention.output.dense.bias', 'bert.encoder.layer.3.crossattention.output.dense.bias', 'bert.encoder.layer.3.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.0.crossattention.self.key.bias', 'bert.encoder.layer.1.crossattention.output.dense.bias', 'bert.encoder.layer.1.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.0.crossattention.self.query.weight', 'bert.encoder.layer.4.crossattention.self.value.bias', 'bert.encoder.layer.10.crossattention.self.query.bias', 'bert.encoder.layer.9.crossattention.output.dense.weight', 'bert.encoder.layer.6.crossattention.self.query.weight']\n",
+            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+          ]
+        }
+      ],
+      "source": [
+        "from transformers import VisionEncoderDecoderModel\n",
+        "\n",
+        "# initialize a vit-bert from a pretrained ViT and a pretrained BERT model. Note that the cross-attention layers will be randomly initialized\n",
+        "model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained(\n",
+        "    \"google/vit-base-patch16-224-in21k\", \"bert-base-uncased\"\n",
+        ")\n",
+        "# saving model after fine-tuning\n",
+        "model.save_pretrained(\"./vit-bert\")\n",
+        "# load fine-tuned model\n",
+        "model = VisionEncoderDecoderModel.from_pretrained(\"./vit-bert\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pne6-SBQZEHJ"
+      },
+      "source": [
+        "An example use case is image captioning. See this model: https://huggingface.co/ydshieh/vit-gpt2-coco-en"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 63,
+      "metadata": {
+        "colab": {
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+          "height": 273,
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+            ]
+          },
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+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "bfa0bcadcc1e4911bc13c76ec46387a0",
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+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from transformers import AutoTokenizer\n",
+        "\n",
+        "repo_name = \"ydshieh/vit-gpt2-coco-en\"\n",
+        "\n",
+        "feature_extractor = ViTFeatureExtractor.from_pretrained(repo_name)\n",
+        "tokenizer = AutoTokenizer.from_pretrained(repo_name)\n",
+        "model = VisionEncoderDecoderModel.from_pretrained(repo_name)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 184,
+      "metadata": {
+        "id": "1f9oGik3UCYo"
+      },
+      "outputs": [],
+      "source": [
+        "pixel_values = feature_extractor(image, return_tensors=\"pt\").pixel_values\n",
+        "\n",
+        "# autoregressively generate text (using beam search or other decoding strategy)\n",
+        "generated_ids = model.generate(pixel_values, max_length=16, num_beams=4, return_dict_in_generate=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 185,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "jung3VncUUnP",
+        "outputId": "d9d64f17-1ba8-4d72-95f5-91b9aaf98fcf"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "['a cat laying on top of a couch next to another cat']\n"
+          ]
+        }
+      ],
+      "source": [
+        "# decode into text\n",
+        "preds = tokenizer.batch_decode(generated_ids[0], skip_special_tokens=True)\n",
+        "preds = [pred.strip() for pred in preds]\n",
+        "print(preds)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6ovNx391UNhQ"
+      },
+      "source": [
+        "### Others: Data2Vec, Perceiver, FLAVA\n",
+        "\n",
+        "There are other multi-modal models in the library, including Deepmind's [Perceiver IO](https://huggingface.co/docs/transformers/model_doc/perceiver) and Meta's [Data2Vec](https://huggingface.co/docs/transformers/model_doc/data2vec) and [FLAVA](https://huggingface.co/docs/transformers/main/en/model_doc/flava), which are Transformer-based models that work on several modalities.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Sc3HkbZpUy2Z"
+      },
+      "source": [
+        "## ONNX\n",
+        "\n",
+        "We also provide support for exporting models in 🤗 Transformers to ONNX, which is an efficient format for running models in production. \n",
+        "\n",
+        "See Tweet: https://twitter.com/NielsRogge/status/1509822585013518367\n",
+        "\n",
+        "Docs: \n",
+        "\n",
+        "* exporting models to ONNX: https://huggingface.co/docs/transformers/serialization\n",
+        "\n",
+        "Models can be made even more efficient with techniques like pruning, quantization, tailored to specific hardware.\n",
+        "* 🤗 Optimum: https://github.com/huggingface/optimum\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tOVH5buJxerf"
+      },
+      "source": [
+        "## Gradio \n",
+        "\n",
+        "When you have a machine learning model, it's great to showcase it to the community, your colleagues, etc.!\n",
+        "\n",
+        "[Gradio](https://gradio.app/) is a great solution for creating a demo for your model blazingly fast. In like, 5 mins.\n",
+        "\n",
+        "Examples:\n",
+        "* https://huggingface.co/spaces/nielsr/perceiver-image-classification\n",
+        "* https://huggingface.co/spaces/dalle-mini/dalle-mini\n",
+        "\n",
+        "\n",
+        "The links above are Gradio demos hosted as a 🤗 [Space](https://huggingface.co/spaces/launch), which is basically a hosting solution for ML demos. 🤗 Spaces also support Streamlit and static web pages."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 64,
+      "metadata": {
+        "id": "0R3IsPj_ml-6"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -q gradio"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 65,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 809
+        },
+        "id": "UWIOchirmk5_",
+        "outputId": "780b8796-1d08-48e4-ef94-f4b7f1c62e84"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Fetching model from: https://huggingface.co/google/vit-base-patch16-224\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/gradio/deprecation.py:40: UserWarning: `enable_queue` is deprecated in `Interface()`, please use it within `launch()` instead.\n",
+            "  warnings.warn(value)\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Colab notebook detected. To show errors in colab notebook, set `debug=True` in `launch()`\n",
+            "Running on public URL: https://57059.gradio.app\n",
+            "\n",
+            "This share link expires in 72 hours. For free permanent hosting, check out Spaces (https://huggingface.co/spaces)\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div><iframe src=\"https://57059.gradio.app\" width=\"900\" height=\"500\" allow=\"autoplay; camera; microphone;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "(<gradio.routes.App at 0x7f79a0192690>,\n",
+              " 'http://127.0.0.1:7862/',\n",
+              " 'https://57059.gradio.app')"
+            ]
+          },
+          "execution_count": 65,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Exception in callback None(<Task finishe...> result=None>)\n",
+            "handle: <Handle>\n",
+            "Traceback (most recent call last):\n",
+            "  File \"/usr/lib/python3.7/asyncio/events.py\", line 88, in _run\n",
+            "    self._context.run(self._callback, *self._args)\n",
+            "TypeError: 'NoneType' object is not callable\n"
+          ]
+        }
+      ],
+      "source": [
+        "import gradio as gr\n",
+        "\n",
+        "examples = [['cats.png']]\n",
+        "\n",
+        "gr.Interface.load(\"huggingface/google/vit-base-patch16-224\", examples=examples, enable_queue=True).launch()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fqLpVqkrdDBS"
+      },
+      "source": [
+        "## Resources\n",
+        "\n",
+        "* Transformers docs: https://huggingface.co/docs/transformers/index\n",
+        "* Example notebooks: https://github.com/huggingface/notebooks/blob/main/examples/image_classification.ipynb\n",
+        "* Example scripts (image classification, image pre-training, semantic segmentation): https://github.com/huggingface/transformers/tree/main/examples/pytorch\n",
+        "* Blog posts: \n",
+        "  - image classification: https://huggingface.co/blog/fine-tune-vit\n",
+        "  - semantic segmentation: https://huggingface.co/blog/fine-tune-segformer\n",
+        "* Transformers-tutorials: https://github.com/NielsRogge/Transformers-Tutorials\n",
+        "* Video on ConvNeXT: https://www.youtube.com/watch?v=Hn1IgPY42Bw&t=2216s"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3kaSUMbeRrj5"
+      },
+      "source": [
+        "## To be expected\n",
+        "\n",
+        "Standardization of other tasks:\n",
+        "* object detection\n",
+        "* panoptic segmentation\n",
+        "* pose estimation\n",
+        "* depth estimation\n",
+        "etc.\n",
+        "\n",
+        "Lots of new models! LeViT, MobileViT, GroupViT ...\n",
+        "\n",
+        "## Interested in contributing?\n",
+        "\n",
+        "* Check issues labeled with [\"new model\"](https://github.com/huggingface/transformers/labels/New%20model), [\"good first issue\"](https://github.com/huggingface/transformers/issues?q=good+first+issue+label%3A%22Good+First+Issue%22), [\"good second issue\"](https://github.com/huggingface/transformers/issues?q=good+first+issue+label%3A%22Good+Second+Issue%22)\n",
+        "* Guides:\n",
+        "  - https://huggingface.co/docs/transformers/contributing\n",
+        "  - https://huggingface.co/docs/transformers/add_new_model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "y8wk440mUzQF"
+      },
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "collapsed_sections": [],
+      "name": "HuggingFace vision ecosystem: overview (June 2022).ipynb",
+      "provenance": []
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diff --git a/computer-vision-study-group/README.md b/computer-vision-study-group/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..afdb7a90a2b4e9f661d0a1ee99a905ce2d22f5fe
--- /dev/null
+++ b/computer-vision-study-group/README.md
@@ -0,0 +1,15 @@
+# Computer Vision Study Group
+
+This is a collection of all past sessions that have been held as part of the Hugging Face Computer Vision Study Group.
+
+| |Session Name | Session Link  |
+|--- |--- | --- |
+|❓|How Do Vision Transformers Work? | [Session Sheet](Sessions/HowDoVisionTransformersWork.md) |
+|🔅|Polarized Self-Attention | [Session Sheet](Sessions/PolarizedSelfAttention.md)|
+|🍄|Swin Transformer | [Session Sheet](Sessions/SwinTransformer.md)|
+|🔮|Introduction to Neural Radiance Fields | [Session Sheet](Sessions/NeuralRadianceFields.md)|
+|🌐|Hugging Face Vision Ecosystem Overview (June 2022) | [Session Sheet](Sessions/HFVisionEcosystem.md)|
+|🪂|Masked Autoencoders Are Scalable Vision Learners | [Session Sheet](Sessions/MaskedAutoEncoders.md)|
+|🦊|Fiber: Coarse-to-Fine Vision-Language Pre-Training | [Session Sheet](Sessions/Fiber.md)|
+|⚔️ |FlexiViT: One Model for All Patch Sizes| [Session Sheet](Sessions/FlexiViT.md)|
+|🤖|BLIP-2: Bootstrapping Language-Image Pre-training| [Session Sheet](Sessions/Blip2.md)|
diff --git a/computer-vision-study-group/Sessions/Blip2.md b/computer-vision-study-group/Sessions/Blip2.md
new file mode 100644
index 0000000000000000000000000000000000000000..b97b89ad654c41b50ea7da1e8efc599de6249255
--- /dev/null
+++ b/computer-vision-study-group/Sessions/Blip2.md
@@ -0,0 +1,25 @@
+# BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
+Session by [johko](https://github.com/johko)
+
+
+## Recording 📺
+[YouTube](https://www.youtube.com/watch?v=k0DAtZCCl1w&pp=ygUdaHVnZ2luZyBmYWNlIHN0dWR5IGdyb3VwIHN3aW4%3D)
+
+
+## Session Slides 🖥️
+[Google Drive](https://docs.google.com/presentation/d/1Y_8Qu0CMlt7jvCd8Jw0c_ILh8LHB0XgnlrvXObe5FYs/edit?usp=sharing)
+
+
+## Original Paper 📄
+[Hugging Face](https://huggingface.co/papers/2301.12597) /
+[arxiv](https://arxiv.org/abs/2301.12597)
+
+
+## GitHub Repo 🧑🏽‍💻
+https://github.com/salesforce/lavis
+
+
+## Additional Resources 📚
+- [BLIP-2 Demo Space](https://huggingface.co/spaces/hysts/BLIP2-with-transformers)
+- [BLIP-2 Transformers Example Notebooks](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/BLIP-2) by Niels Rogge
+- [BLIP-2 Transformers Docs](https://huggingface.co/docs/transformers/model_doc/blip-2)
diff --git a/computer-vision-study-group/Sessions/Fiber.md b/computer-vision-study-group/Sessions/Fiber.md
new file mode 100644
index 0000000000000000000000000000000000000000..4d150247fec7cb7f7b81fdd89f909ce41da946d1
--- /dev/null
+++ b/computer-vision-study-group/Sessions/Fiber.md
@@ -0,0 +1,24 @@
+# Fiber: Coarse-to-Fine Vision-Language Pre-Training with Fusion in the Backbone
+Session by [johko](https://github.com/johko)
+
+
+## Recording 📺
+[YouTube](https://www.youtube.com/watch?v=m9qhNGuWE2g&t=20s&pp=ygUdaHVnZ2luZyBmYWNlIHN0dWR5IGdyb3VwIHN3aW4%3D)
+
+
+## Session Slides 🖥️
+[Google Drive](https://docs.google.com/presentation/d/1vSu27tE87ZM103_CkgqsW7JeIp2mrmyl/edit?usp=sharing&ouid=107717747412022342990&rtpof=true&sd=true)
+
+
+## Original Paper 📄
+[Hugging Face](https://huggingface.co/papers/2206.07643) /
+[arxiv](https://arxiv.org/abs/2206.07643)
+
+
+## GitHub Repo 🧑🏽‍💻
+https://github.com/microsoft/fiber
+
+
+## Additional Resources 📚
+- [Text to Pokemon](https://huggingface.co/spaces/lambdalabs/text-to-pokemon) HF Space to create your own Pokemon
+- [Paper to Pokemon](https://huggingface.co/spaces/hugging-fellows/paper-to-pokemon) derived from the above space - create your own Pokemon from a paper
\ No newline at end of file
diff --git a/computer-vision-study-group/Sessions/FlexiViT.md b/computer-vision-study-group/Sessions/FlexiViT.md
new file mode 100644
index 0000000000000000000000000000000000000000..0a509421c5abdce45697f055a4eba6c2bdf7b504
--- /dev/null
+++ b/computer-vision-study-group/Sessions/FlexiViT.md
@@ -0,0 +1,23 @@
+# FlexiViT: One Model for All Patch Sizes
+Session by [johko](https://github.com/johko)
+
+
+## Recording 📺
+[YouTube](https://www.youtube.com/watch?v=TlRYBgsl7Q8&t=977s&pp=ygUdaHVnZ2luZyBmYWNlIHN0dWR5IGdyb3VwIHN3aW4%3D)
+
+
+## Session Slides 🖥️
+[Google Drive](https://docs.google.com/presentation/d/1rLAYr160COYQMUN0FDH7D9pP8qe1_QyXGvfbHkutOt8/edit?usp=sharing)
+
+
+## Original Paper 📄
+[Hugging Face](https://huggingface.co/papers/2212.08013) /
+[arxiv](https://arxiv.org/abs/2212.08013)
+
+
+## GitHub Repo 🧑🏽‍💻
+https://github.com/google-research/big_vision
+
+
+## Additional Resources 📚
+- [FlexiViT PR](https://github.com/google-research/big_vision/pull/24)
diff --git a/computer-vision-study-group/Sessions/HFVisionEcosystem.md b/computer-vision-study-group/Sessions/HFVisionEcosystem.md
new file mode 100644
index 0000000000000000000000000000000000000000..806dc2b11eac09ec68d29e20758e75e660d56904
--- /dev/null
+++ b/computer-vision-study-group/Sessions/HFVisionEcosystem.md
@@ -0,0 +1,10 @@
+# Hugging Face Vision Ecosystem Overview (June 2022)
+Session by [Niels Rogge](https://github.com/NielsRogge)
+
+
+## Recording 📺
+[YouTube](https://www.youtube.com/watch?v=oL-xmufhZM8&pp=ygUdaHVnZ2luZyBmYWNlIHN0dWR5IGdyb3VwIHN3aW4%3D)
+
+
+## Additional Resources 📚
+- [Accompanying Notebook](../Notebooks/HuggingFace_vision_ecosystem_overview_(June_2022).ipynb)
\ No newline at end of file
diff --git a/computer-vision-study-group/Sessions/HowDoVisionTransformersWork.md b/computer-vision-study-group/Sessions/HowDoVisionTransformersWork.md
new file mode 100644
index 0000000000000000000000000000000000000000..053ff83616d2da2937b2a8e55001d8b0a371e595
--- /dev/null
+++ b/computer-vision-study-group/Sessions/HowDoVisionTransformersWork.md
@@ -0,0 +1,27 @@
+# How Do Vision Transformers Work
+Session by [johko](https://github.com/johko)
+
+
+## Session Slides 🖥️
+[Google Drive](https://docs.google.com/presentation/d/1PewOHVABkxx0jO9PoJSQi8to_WNlL4HdDp4M9e4L8hs/edit?usp=drivesdks)
+
+
+## Original Paper 📄
+[Hugging Face](https://huggingface.co/papers/2202.06709) /
+[arxiv](https://arxiv.org/pdf/2202.06709.pdf)
+
+
+## GitHub Repo 🧑🏽‍💻
+https://github.com/microsoft/Swin-Transformer
+
+
+## Additional Resources 📚
+Hessian Matrices:
+
+- https://stackoverflow.com/questions/23297090/how-calculating-hessian-works-for-neural-network-learning
+- https://machinelearningmastery.com/a-gentle-introduction-to-hessian-matrices/
+
+Loss Landscape Visualization:
+
+- https://mathformachines.com/posts/visualizing-the-loss-landscape/
+- https://github.com/tomgoldstein/loss-landscape
\ No newline at end of file
diff --git a/computer-vision-study-group/Sessions/MaskedAutoEncoders.md b/computer-vision-study-group/Sessions/MaskedAutoEncoders.md
new file mode 100644
index 0000000000000000000000000000000000000000..ce841899e7531a401aadf539b39a8f934f65c7ff
--- /dev/null
+++ b/computer-vision-study-group/Sessions/MaskedAutoEncoders.md
@@ -0,0 +1,24 @@
+# Masked Autoencoders are Scalable Vision Learners
+Session by [johko](https://github.com/johko)
+
+
+## Recording 📺
+[YouTube](https://www.youtube.com/watch?v=AC6flxUFLrg&pp=ygUdaHVnZ2luZyBmYWNlIHN0dWR5IGdyb3VwIHN3aW4%3D)
+
+
+## Session Slides 🖥️
+[Google Drive](https://docs.google.com/presentation/d/10ZZ-Rl1D57VX005a58OmqNeOB6gPnE54/edit?usp=sharing&ouid=107717747412022342990&rtpof=true&sd=true)
+
+
+## Original Paper 📄
+[Hugging Face](https://huggingface.co/papers/2111.06377) /
+[arxiv](https://arxiv.org/abs/2111.06377)
+
+
+## GitHub Repo 🧑🏽‍💻
+https://github.com/facebookresearch/mae
+
+
+## Additional Resources 📚
+- [Transformers Docs ViTMAE](https://huggingface.co/docs/transformers/model_doc/vit_mae)
+- [Transformers ViTMAE Demo Notebook](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/ViTMAE) by Niels Rogge
\ No newline at end of file
diff --git a/computer-vision-study-group/Sessions/NeuralRadianceFields.md b/computer-vision-study-group/Sessions/NeuralRadianceFields.md
new file mode 100644
index 0000000000000000000000000000000000000000..47d61e1deb632dcaa2fba89492931c18a019d59d
--- /dev/null
+++ b/computer-vision-study-group/Sessions/NeuralRadianceFields.md
@@ -0,0 +1,19 @@
+# Introduction to Neural Radiance Fields
+Session by [Aritra](https://arig23498.github.io/) and [Ritwik](ritwikraha.github.io)
+
+
+## Recording 📺
+[YouTube](https://www.youtube.com/watch?v=U2XS7SxOy2s)
+
+
+## Session Slides 🖥️
+[Google Drive](https://docs.google.com/presentation/d/e/2PACX-1vTQVnoTJGhRxDscNV1Mg2aYhvXP8cKODpB5Ii72NWoetCGrTLBJWx_UD1oPXHrzPtj7xO8MS_3TQaSH/pub?start=false&loop=false&delayms=3000)
+
+
+## Original Paper 📄
+[Hugging Face](https://huggingface.co/papers/2003.08934) /
+[arxiv](https://arxiv.org/abs/2003.08934)
+
+
+## GitHub Repo 🧑🏽‍💻
+https://github.com/bmild/nerf
diff --git a/computer-vision-study-group/Sessions/PolarizedSelfAttention.md b/computer-vision-study-group/Sessions/PolarizedSelfAttention.md
new file mode 100644
index 0000000000000000000000000000000000000000..6769ec49b8c8458f70dda0030bb87ec2ad68dd63
--- /dev/null
+++ b/computer-vision-study-group/Sessions/PolarizedSelfAttention.md
@@ -0,0 +1,14 @@
+# Polarized Self-Attention
+Session by [Satpal](https://github.com/satpalsr)
+
+## Session Slides 🖥️
+[GitHub PDF](https://github.com/satpalsr/Talks/blob/main/PSA_discussion.pdf)
+
+
+## Original Paper 📄
+[Hugging Face](https://huggingface.co/papers/2107.00782) /
+[arxiv](https://arxiv.org/pdf/2107.00782.pdf)
+
+
+## GitHub Repo 🧑🏽‍💻
+https://github.com/DeLightCMU/PSA
diff --git a/computer-vision-study-group/Sessions/SwinTransformer.md b/computer-vision-study-group/Sessions/SwinTransformer.md
new file mode 100644
index 0000000000000000000000000000000000000000..7b213d2647481c1cd73863274aa9145d7f385472
--- /dev/null
+++ b/computer-vision-study-group/Sessions/SwinTransformer.md
@@ -0,0 +1,25 @@
+# Swin Transformer
+Session by [johko](https://github.com/johko)
+
+
+## Recording 📺
+[YouTube](https://www.youtube.com/watch?v=Ngikt-K1Ecc&t=305s&pp=ygUdaHVnZ2luZyBmYWNlIHN0dWR5IGdyb3VwIHN3aW4%3D)
+
+
+## Session Slides 🖥️
+[Google Drive](https://docs.google.com/presentation/d/1RoFIC6vE55RS4WNqSlzNu3ljB6F-_8edtprAFXpGvKs/edit?usp=sharing)
+
+
+## Original Paper 📄
+[Hugging Face](https://huggingface.co/papers/2103.14030) /
+[arxiv](https://arxiv.org/pdf/2103.14030.pdf)
+
+
+## GitHub Repo 🧑🏽‍💻
+https://github.com/xxxnell/how-do-vits-work
+
+
+## Additional Resources 📚
+- [Transformers Docs Swin v1](https://huggingface.co/docs/transformers/model_doc/swin)
+- [Transformers Docs Swin v2](https://huggingface.co/docs/transformers/model_doc/swinv2)
+- [Transformers Docs Swin Super Resolution](https://huggingface.co/docs/transformers/model_doc/swin2sr)
\ No newline at end of file
diff --git a/config.json b/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..8d2a327cb177d5048125acef1f6c6fbab0b47606
--- /dev/null
+++ b/config.json
@@ -0,0 +1,52 @@
+{
+  "_name_or_path": "openai/whisper-small",
+  "activation_dropout": 0.0,
+  "activation_function": "gelu",
+  "apply_spec_augment": false,
+  "architectures": [
+    "WhisperForConditionalGeneration"
+  ],
+  "attention_dropout": 0.0,
+  "begin_suppress_tokens": [
+    220,
+    50257
+  ],
+  "bos_token_id": 50257,
+  "classifier_proj_size": 256,
+  "d_model": 768,
+  "decoder_attention_heads": 12,
+  "decoder_ffn_dim": 3072,
+  "decoder_layerdrop": 0.0,
+  "decoder_layers": 12,
+  "decoder_start_token_id": 50258,
+  "dropout": 0.0,
+  "encoder_attention_heads": 12,
+  "encoder_ffn_dim": 3072,
+  "encoder_layerdrop": 0.0,
+  "encoder_layers": 12,
+  "eos_token_id": 50257,
+  "forced_decoder_ids": null,
+  "init_std": 0.02,
+  "is_encoder_decoder": true,
+  "mask_feature_length": 10,
+  "mask_feature_min_masks": 0,
+  "mask_feature_prob": 0.0,
+  "mask_time_length": 10,
+  "mask_time_min_masks": 2,
+  "mask_time_prob": 0.05,
+  "max_length": 448,
+  "max_source_positions": 1500,
+  "max_target_positions": 448,
+  "median_filter_width": 7,
+  "model_type": "whisper",
+  "num_hidden_layers": 12,
+  "num_mel_bins": 80,
+  "pad_token_id": 50257,
+  "scale_embedding": false,
+  "suppress_tokens": [],
+  "torch_dtype": "float32",
+  "transformers_version": "4.40.0.dev0",
+  "use_cache": false,
+  "use_weighted_layer_sum": false,
+  "vocab_size": 51865
+}
diff --git a/gradio-blocks/README.md b/gradio-blocks/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..3d1595c37a9a3e4512b034d996955742a426b29f
--- /dev/null
+++ b/gradio-blocks/README.md
@@ -0,0 +1,123 @@
+# Welcome to the [Gradio](https://gradio.app/) Blocks Party 🥳
+
+![image (1)](https://user-images.githubusercontent.com/81195143/167954125-9854bf6b-4ae5-4735-8fdd-830fec41efa1.png)
+
+
+_**Timeline**: May 17th, 2022 - May 31st, 2022_
+
+---
+
+We are happy to invite you to the Gradio Blocks Party - a community event in which we will create **interactive demos** for state-of-the-art machine learning models. Demos are powerful because they allow anyone — not just ML engineers — to try out models in the browser, give feedback on predictions, identify trustworthy models. The event will take place from **May 17th to 31st**. We will be organizing this event on [Github](https://github.com/huggingface/community-events) and the [Hugging Face discord channel](https://discord.com/invite/feTf9x3ZSB). Prizes will be given at the end of the event, see: [Prizes](#prizes)
+
+<img src="https://user-images.githubusercontent.com/81195143/168656398-ace7acc9-ef7a-4e90-a9cd-c7d15dd800e1.gif" width="1160" height="600"/>
+
+## What is Gradio?
+
+Gradio is a Python library that allows you to quickly build web-based machine learning demos, data science dashboards, or other kinds of web apps, entirely in Python. These web apps can be launched from wherever you use Python (jupyter notebooks, colab notebooks, Python terminal, etc.) and shared with anyone instantly using Gradio's auto-generated share links. To learn more about Gradio see the Getting Started Guide: https://gradio.app/getting_started/ and the new Course on Huggingface about Gradio: [Gradio Course](https://huggingface.co/course/chapter9/1?fw=pt).
+
+Gradio can be installed via pip and comes preinstalled in Hugging Face Spaces, the latest version of Gradio can be set in the README in spaces by setting the sdk_version for example `sdk_version: 3.0b8`
+
+`pip install gradio` to install gradio locally
+
+
+## What is Blocks?
+
+`gradio.Blocks` is a low-level API that allows you to have full control over the data flows and layout of your application. You can build very complex, multi-step applications using Blocks. If you have already used `gradio.Interface`, you know that you can easily create fully-fledged machine learning demos with just a few lines of code. The Interface API is very convenient but in some cases may not be sufficiently flexible for your needs. For example, you might want to:
+
+* Group together related demos as multiple tabs in one web app.
+* Change the layout of your demo instead of just having all of the inputs on the left and outputs on the right.
+* Have multi-step interfaces, in which the output of one model becomes the input to the next model, or have more flexible data flows in general.
+* Change a component's properties (for example, the choices in a Dropdown) or its visibility based on user input.
+
+To learn more about Blocks, see the [official guide](https://www.gradio.app/introduction_to_blocks/) and the [docs](https://gradio.app/docs/).
+
+## What is Hugging Face Spaces?
+
+Spaces are a simple way to host ML demo apps directly on your profile or your organization’s profile on Hugging Face. This allows you to create your ML portfolio, showcase your projects at conferences or to stakeholders, and work collaboratively with other people in the ML ecosystem. Learn more about Spaces in the [docs](https://huggingface.co/docs/hub/spaces).
+
+## How Do Gradio and Hugging Face work together?
+
+Hugging Face Spaces is a free hosting option for Gradio demos. Spaces comes with 3 SDK options: Gradio, Streamlit and Static HTML demos. Spaces can be public or private and the workflow is similar to github repos. There are over 2000+ Gradio spaces currently on Hugging Face. Learn more about spaces and gradio: https://huggingface.co/docs/hub/spaces
+
+## Event Plan
+
+main components of the event consist of:
+
+1. Learning about Gradio and the new Blocks Feature
+2. Building your own Blocks demo using Gradio and Hugging Face Spaces
+3. Submitting your demo on Spaces to the Gradio Blocks Party Organization
+4. Share your blocks demo with a permanent shareable link 
+5. Win Prizes
+
+
+## Example spaces using Blocks
+
+<img width="1180" alt="mindseye-lite" src="https://user-images.githubusercontent.com/81195143/168619604-cf1ac733-c10e-487f-add4-8da48002dcff.png">
+
+- [dalle-mini](https://huggingface.co/spaces/dalle-mini/dalle-mini)([Code](https://huggingface.co/spaces/dalle-mini/dalle-mini/blob/main/app/gradio/app.py))
+- [mindseye-lite](https://huggingface.co/spaces/multimodalart/mindseye-lite)([Code](https://huggingface.co/spaces/multimodalart/mindseye-lite/blob/main/app.py))
+- [ArcaneGAN-blocks](https://huggingface.co/spaces/akhaliq/ArcaneGAN-blocks)([Code](https://huggingface.co/spaces/akhaliq/ArcaneGAN-blocks/blob/main/app.py))
+- [gr-blocks](https://huggingface.co/spaces/merve/gr-blocks)([Code](https://huggingface.co/spaces/merve/gr-blocks/blob/main/app.py))
+- [tortoisse-tts](https://huggingface.co/spaces/osanseviero/tortoisse-tts)([Code](https://huggingface.co/spaces/osanseviero/tortoisse-tts/blob/main/app.py))
+- [CaptchaCracker](https://huggingface.co/spaces/osanseviero/tortoisse-tts)([Code](https://huggingface.co/spaces/akhaliq/CaptchaCracker/blob/main/app.py))
+
+
+## To participate in the event
+
+- Join the organization for Blocks event
+    - [https://huggingface.co/Gradio-Blocks](https://huggingface.co/Gradio-Blocks)
+- Join the discord
+    - [discord](https://discord.com/invite/feTf9x3ZSB)
+
+
+Participants will be building and sharing Gradio demos using the Blocks feature. We will share a list of ideas of spaces that can be created using blocks or participants are free to try out their own ideas. At the end of the event, spaces will be evaluated and prizes will be given.
+
+
+## Potential ideas for creating spaces:
+
+
+- Trending papers from https://paperswithcode.com/
+- Models from huggingface model hub: https://huggingface.co/models
+- Models from other model hubs
+    - Tensorflow Hub: see example Gradio demos at https://huggingface.co/tensorflow
+    - Pytorch Hub: see example Gradio demos at https://huggingface.co/pytorch
+    - ONNX model Hub: see example Gradio demos at https://huggingface.co/onnx
+    - PaddlePaddle Model Hub: see example Gradio demos at https://huggingface.co/PaddlePaddle
+- participant ideas, try out your own ideas
+
+
+## Prizes
+- 1st place winner based on likes
+    - [Hugging Face PRO subscription](https://huggingface.co/pricing) for 1 year
+    - Embedding your Gradio Blocks demo in the Gradio Blog
+- top 10 winners based on likes
+    - Swag from [Hugging Face merch shop](https://huggingface.myshopify.com/): t-shirts, hoodies, mugs of your choice
+- top 25 winners based on likes
+    - [Hugging Face PRO subscription](https://huggingface.co/pricing) for 1 month
+- Blocks event badge on HF for all participants!
+
+## Prizes Criteria
+
+- Staff Picks
+- Most liked Spaces
+- Community Pick (voting)
+- Most Creative Space (voting)
+- Most Educational Space (voting)
+- CEO's pick (one prize for a particularly impactful demo), picked by @clem
+- CTO's pick (one prize for a particularly technically impressive demo), picked by @julien
+
+
+## Creating a Gradio demo on Hugging Face Spaces
+
+Once a model has been picked from the choices above or feel free to try your own idea, you can share a model in a Space using Gradio
+
+Read more about how to add [Gradio spaces](https://huggingface.co/blog/gradio-spaces).
+ 
+Steps to add Gradio Spaces to the Gradio Blocks Party org
+1. Create an account on Hugging Face
+2. Join the Gradio Blocks Party Organization by clicking "Join Organization" button in the organization page or using the shared link above
+3. Once your request is approved, add your space using the Gradio SDK and share the link with the community!
+
+## LeaderBoard for Most Popular Blocks Event Spaces based on Likes
+
+- See Leaderboard: https://huggingface.co/spaces/Gradio-Blocks/Leaderboard
diff --git a/huggan/README.md b/huggan/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..12f7933f47a4684de0d76cbeb21a3fe1d6f9d3e8
--- /dev/null
+++ b/huggan/README.md
@@ -0,0 +1,487 @@
+# HugGAN Sprint
+
+![Banner](assets/huggan_banner.png?raw=true "Banner")
+
+_**Timeline**: April 4th, 2022 - April 17th, 2022_
+
+---
+
+Welcome to HugGAN Sprint! The goal of this sprint is to add more GANs and GAN-based demos to the Hugging Face Hub 🤗.
+
+During the sprint, we’ll be bringing in some awesome speakers to talk about GANs and the future of generative models. Oh, and if you need access to compute for your project, we’ll help you there too! As an added bonus, if you choose to participate, we’ll send you a gift (specific details TBD). We encourage you to form teams of ~2-3 people! Make friends in the Discord :)
+
+To join:
+
+1. Fill out [this form](https://forms.gle/goq41UgzsvuKKTFFA), so we can keep track of who’s joining.
+2. Send a reaction in the [#join-sprint channel](https://discord.com/channels/879548962464493619/954070850645135462) under the HugGAN category in Discord. This will add you to the rest of the related channels. If you haven't joined our discord yet, [click here](https://discord.gg/H3bUrDPTfS).
+3. Once you’ve decided what you want to work on, add your project’s information to [this sheet](https://docs.google.com/spreadsheets/d/1aAHqOOk2SOw4j6mrJLkLT6ZyKyLDOvGF5D9tuUqnoG8/edit#gid=0), where you can describe your project and let us know if you need additional compute. Still brainstorming? Feel free to propose ideas in #sprint-discussions.
+
+## Table of Contents
+
+- [Important dates](#important-dates)
+- [How to install relevant libraries](#how-to-install-relevant-libraries)
+- [General workflow](#general-workflow)
+- [Datasets to add](#datasets-to-add)
+- [Links to check out](#links-to-check-out)
+- [GAN metrics](#gan-metrics)
+- [Evaluation](#evaluation)
+- [Prizes](#prizes)
+- [Communication and Problems](#communication-and-problems)
+- [Talks](#talks)
+- [General Tips & Tricks](#general-tips-and-tricks)
+
+## Important dates
+
+| Date      | Description |
+| ----------- | ----------- |
+| April 4th      | Sprint Kickoff 🚀      |
+| April 15th   | Submission Deadline 🛑  |
+| April 22nd | Prizes Announced for Participants 🎁 |
+
+## How to install relevant libraries
+
+You'll need the following dependencies installed to use this repo:
+
+- [PyTorch](https://pytorch.org/) or [Keras](https://keras.io/) - depending on which framework you prefer ;)
+- [🤗 Datasets](https://huggingface.co/docs/datasets/index)
+- [🤗 Accelerate](https://huggingface.co/docs/accelerate/index) - in case you're planning to train a PyTorch model and you want it to be run effortlessly 
+
+We recommend installing the above libraries in a [virtual environment](https://docs.python.org/3/library/venv.html). 
+If you're unfamiliar with Python virtual environments, check out the [user guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/). Create a virtual environment with the version of Python you're going to use and activate it.
+
+You should be able to run the command:
+
+```bash
+python3 -m venv <your-venv-name>
+```
+
+You can activate your venv by running
+
+```bash
+source ~/<your-venv-name>/bin/activate
+```
+
+### Install Dependencies
+
+We've packaged up the example scripts here into a simple Python package. To install it, just pip install it
+
+```
+git clone https://github.com/huggingface/community-events.git
+cd community-events
+pip install .
+```
+
+If you use `pip install -e .` instead of `pip install`, it will install the package in development mode, which can be useful if you are planning on contributing any changes here 🤗. 
+
+## General workflow
+
+The process to follow is outlined below. It consists of 3 steps:
+
+1. Get a dataset and push to the Hub
+2. Train a model and push to the Hub
+3. Create a demo (🤗 Space)
+
+These steps are explained in more detail below.
+
+### 1. Get a dataset and push to Hub
+
+The first step is the most obvious one: to train a GAN (or any neural network), we need a dataset. This could be either a dataset that is already available on the [Hub](https://huggingface.co/datasets), or one that isn't already. Below we'll explain how to load the data in both cases.
+
+Note that we maintain a list of interesting datasets to add to the Hub [here](#datasets-to-add).
+
+#### 1.1 Use a dataset already available on the Hub
+
+Most famous computer vision datasets are already available on the [Hub](https://huggingface.co/datasets?task_categories=task_categories:image-classification) (such as [MNIST](https://huggingface.co/datasets/mnist), [Fashion MNIST](https://huggingface.co/datasets/fashion_mnist), [CIFAR-10](https://huggingface.co/datasets/cifar10), [CIFAR-100](https://huggingface.co/datasets/cifar100), etc.).
+
+Loading a dataset can be done as follows:
+
+```python
+from datasets import load_dataset
+
+# a general one ...
+dataset = load_dataset("mnist")
+
+# ... or one that's part of the huggan organization
+dataset = load_dataset("huggan/edges2shoes")
+```
+
+In a notebook, you can **directly see** the images by selecting a split and then the appropriate column:
+
+```python
+example = dataset['train'][0]
+print(example['image'])
+```
+
+#### 1.2 Upload a new dataset to the Hub
+
+In case your dataset is not already on the Hub, you can upload it to the `huggan` [organization](https://huggingface.co/huggan). If you've signed up for the event by filling in the [spreadsheet]((https://docs.google.com/spreadsheets/d/1aAHqOOk2SOw4j6mrJLkLT6ZyKyLDOvGF5D9tuUqnoG8/edit#gid=0)), your Hugging Face account should be part of it. 
+
+Let's illustrate with an example how this was done for NVIDIA's [MetFaces dataset](https://github.com/NVlabs/metfaces-dataset):
+
+<p align="center">
+    <img src="https://github.com/NVlabs/metfaces-dataset/blob/master/img/metfaces-teaser.png" alt="drawing" width="700"/>
+</p>
+
+Previously, this dataset was only hosted on [Google Drive](https://github.com/NVlabs/metfaces-dataset#overview), and not really easily accessible.
+
+To begin with, one should check that one is correctly logged in and that `git-lfs` is installed so that the dataset can be uploaded.
+
+Run:
+
+```bash
+huggingface-cli login
+```
+
+in a terminal, or case you're working in a notebook
+
+```python
+from huggingface_hub import notebook_login
+
+notebook_login()
+```
+
+It is recommended to login with your access token that can be found under your HuggingFace profile (icon in the top right corner on [hf.co](http://hf.co/), then Settings -> Access Tokens -> User Access Tokens -> New Token (if you haven't generated one already). Alternatively, you can go to [your token settings](https://huggingface.co/settings/tokens) directly.
+
+You can then copy-paste this token to log in locally.
+
+Next, let's make sure that `git-lfs` is correctly installed. To so, simply run:
+
+```bash
+git-lfs -v
+```
+
+The output should show something like `git-lfs/2.13.2 (GitHub; linux amd64; go 1.15.4)`. If your console states that the `git-lfs` command was not found, please make sure to install it [here](https://git-lfs.github.com/) or simply via: 
+
+```bash
+sudo apt-get install git-lfs
+git config --global user.email "you@example.com"
+git config --global user.name "Your Name"
+```
+
+Next, one can leverage the [`ImageFolder`](https://huggingface.co/docs/datasets/v2.0.0/en/image_process#imagefolder) builder to very easily upload an image dataset to the hub. In case the dataset you're uploading has a direct download URL, you can simply provide it to the `data_files` argument as shown below. Otherwise, you'll need to go to the link of the dataset and manually download it first as a zip/tar (which was the case for MetFaces), and provide the file through the `data_files` argument. Alternatively, it may be that you have a folder with images, in which case you can provide it using the `data_dir` argument. Note that the latter assumes a [particular structure](https://huggingface.co/docs/datasets/v2.0.0/en/image_process#imagefolder).
+
+```python
+from datasets import load_dataset
+
+# option 1: local folder
+dataset = load_dataset("imagefolder", data_dir="path_to_folder")
+# option 2: local or remote file(s), supporting the following extensions: tar, gzip, zip, xz, rar, zstd
+dataset = load_dataset("imagefolder", data_files="path_to_file_or_direct_download_link")
+
+# note that you can also provide them as separate splits, like so:
+dataset = load_dataset("imagefolder", data_files={"train": ["path/to/file1", "path/to/file2"], "test": ["path/to/file3", "path/to/file4"]})
+```
+
+Once you've loaded your dataset, you can push it to the Hub with a single line of code:
+
+```python
+dataset.push_to_hub("huggan/name-of-your-dataset")
+```
+
+Et voila! Your dataset is now available on the Hub :) If you wait a bit, the Dataset viewer should be able to preview images in the browser. The MetFaces dataset can be seen here: https://huggingface.co/datasets/huggan/metfaces. 
+
+<p align="center">
+    <img src="https://github.com/huggingface/community-events/blob/main/huggan/assets/metfaces.png" alt="drawing" width="700"/>
+</p>
+
+The cool thing is that anyone can now access this dataset from anywhere, using `load_dataset` 🎉🥳 this means that you can easily load the dataset on another computer for instance, or in a different environment. Amazing, isn't it?
+
+❗ Note: When uploading a dataset, make sure that it has appropriate column names. The `ImageFolder` utility automatically creates `image` and `label` columns, however if there's only one image class, it makes sense to remove the `label` column before pushing to the hub. This can be done as follows:
+
+```python
+dataset = dataset.remove_columns("label")
+```
+
+Note that you can always update a dataset by simply calling `push_to_hub` again (providing the same name).
+
+#### 1.3 Processing the data
+
+Once you've uploaded your dataset, you can load it and create a dataloader for it. The code example below shows how to apply some data augmentation and creating a PyTorch Dataloader (the [PyTorch example scripts](pytorch) all leverage this). More info can also be found in the [docs](https://huggingface.co/docs/datasets/v2.0.0/en/image_process#process-image-data).
+
+```python
+from datasets import load_dataset
+from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
+from torch.utils.data import DataLoader
+
+# load your data
+dataset = load_dataset("dataset_name")
+
+image_size = 256
+
+# define image transformations (e.g. using torchvision)
+transform = Compose(
+    [
+        Resize(image_size),
+        CenterCrop(image_size),
+        ToTensor(),
+        Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
+    ]
+)
+
+# define function
+def transforms(examples):
+   examples["image"] = [transform(image.convert("RGB")) for image in examples["image"]]
+
+   return examples
+
+transformed_dataset = dataset.with_transform(transforms)
+
+# create dataloader
+dataloader = DataLoader(
+     transformed_dataset["train"], batch_size="your batch size", shuffle=True, num_workers="your number of CPU cores"
+)
+``` 
+
+As can be seen, we leverage the [`with_transform`](https://huggingface.co/docs/datasets/v2.0.0/en/package_reference/main_classes#datasets.Dataset.with_transform) method here, which will make sure the image transformations will only be performed when iterating over the data (i.e. data augmentation is performed on-the-fly, making it very RAM-friendly) rather than performing it on the entire dataset in one go (which would be the case if you use [`map`](https://huggingface.co/docs/datasets/v2.0.0/en/package_reference/main_classes#datasets.Dataset.map)). The `with_transform` method does the same thing as [`set_transform`](https://huggingface.co/docs/datasets/v2.0.0/en/package_reference/main_classes#datasets.Dataset.set_transform), except that it does return a new `Dataset` rather than performing the operation in-place.
+
+### 2. Train a model and push to Hub
+
+Next, one can start training a model. This could be any model you'd like. However, we provide some example scripts to help you get started, in both [PyTorch](pytorch) and [Tensorflow](tensorflow). An example is the [DCGAN](pytorch/dcgan) model for unconditional image generation. Simply follow the README that explains all the details of the relevant implementation, and run it in your environment. 
+
+The PyTorch example scripts all leverage 🤗 [Accelerate](https://huggingface.co/docs/accelerate/index), which provides an easy API to make your scripts run on any kind of distributed setting (multi-GPUs, TPUs etc.) and with mixed precision, while still letting you write your own training loop. 
+
+Alternatively, we also provide a [Links to Check Out](#links-to-check-out) section to give you some inspiration.
+
+Below, we explain in more detail how to upload your model to the Hub, depending on the framework you're using (sections [2.1](#21-pytorch) and [2.2](#22-keras)). In section [2.3](#33-alternative-ways-to-upload-a-model-to-the-hub), we'll explain how to write a nice model card. In section [2.4](24-model-cards), we'll illustrate alternative ways to upload (and re-use) a model to (and from) the hub. Finally, in section [2.5](25-accelerate), we explain 🤗 [Accelerate](https://huggingface.co/docs/accelerate/index), the awesome library that makes training PyTorch models on any kind of environment a breeze. Be sure to check it out!
+
+#### 2.1 PyTorch
+
+If you're planning to train a custom PyTorch model, it's recommended to make it inherit from `PyTorchModelHubMixin`. This makes sure you can push it to the Hub at the end of training, and reload it afterwards using `from_pretrained`, as shown in the code example below:
+
+```python
+from huggingface_hub import PyTorchModelHubMixin
+
+class MyGenerator(nn.Module, PyTorchModelHubMixin):
+   def __init__(self, **kwargs):
+      super().__init__()
+      self.config = kwargs.pop("config", None)
+      self.layer = ...
+   def forward(self, ...):
+      return ...
+
+# Create model
+model = MyGenerator()
+
+# Push to HuggingFace Hub
+model.push_to_hub("huggan/name-of-your-model").
+
+# Reload from HuggingFace Hub
+reloaded = MyGenerator.from_pretrained("huggan/name-of-your-model").
+```
+
+This `PyTorchModelHubMixin` class is available in the [`huggingface_hub` library](https://github.com/huggingface/huggingface_hub), which comes pre-installed if you install `datasets` (or `transformers`) in your environment.
+
+#### 2.2 Keras
+
+In Keras, one can leverage the `push_to_hub_keras` and `from_pretrained_keras` methods:
+
+```python
+import tensorflow as tf
+from huggingface_hub import push_to_hub_keras, from_pretrained_keras
+
+# Build a Keras model
+inputs = tf.keras.layers.Input(shape=(2,))
+x = tf.keras.layers.Dense(2, activation="relu")(inputs)
+model = tf.keras.models.Model(inputs=inputs, outputs=x)
+model.compile(optimizer="adam", loss="mse")
+
+# Push to HuggingFace Hub
+push_to_hub_keras(model, "huggan/my-cool-model")
+
+# Reload from HuggingFace Hub
+reloaded = from_pretrained_keras("huggan/my-cool-model")
+```
+
+These methods are available in the [`huggingface_hub` library](https://github.com/huggingface/huggingface_hub), which comes pre-installed if you install `datasets` (or `transformers`) in your environment. Note that the `push_to_hub_keras` method supports pushing several models (such as a generator and discriminator) to the same repo, as illustrated [here](https://github.com/huggingface/huggingface_hub/issues/533#issuecomment-1058093158).
+
+#### 2.3 Alternative ways to upload a model to the Hub
+
+Besides the methods explained in sections 2.1 and 2.2 above, you can also share model assets directly from git, which is explained in depth in [this guide](https://huggingface.co/docs/hub/adding-a-model#uploading-your-files).
+
+#### 2.4 Model cards
+
+When uploading a model to the Hub, it's important to include a so-called [model card](https://huggingface.co/course/chapter4/4?fw=pt) with it. This is just a README (in Markdown) 🃏 that includes:
+- license,
+- task,
+- `huggan` and `gan` tags,
+- dataset metadata,
+- information related to the model,
+- information on dataset, intended uses,
+- a model output.
+
+If you trained one of the example models, this model card will be automatically generated for you. If you didn’t train the model yourself, be sure to both credit the original authors and include the associated license in your model card! Here is an [example model repo](https://huggingface.co/merve/anime-faces-generator).
+
+You can also use this [template model card](model_card_template.md)
+ as a guide to build your own.
+
+![Alt text](assets/example_model.png?raw=true "Title")
+
+#### 2.5 Accelerate
+
+HuggingFace `accelerate` is an awesome library for training PyTorch models. Here we show why. 
+
+Basically, the library requires to replace this:
+
+```
+my_model.to(device)
+
+for batch in my_training_dataloader:
+    my_optimizer.zero_grad()
+    inputs, targets = batch
+    inputs = inputs.to(device)
+    targets = targets.to(device)
+    outputs = my_model(inputs)
+    loss = my_loss_function(outputs, targets)
+    loss.backward()
+    my_optimizer.step()
+```    
+    
+by this:
+
+```diff
++ from accelerate import Accelerator
+
++ accelerator = Accelerator()
+- my_model.to(device)
+  # Pass every important object (model, optimizer, dataloader) to *accelerator.prepare*
++ my_model, my_optimizer, my_training_dataloader = accelerate.prepare(
++     my_model, my_optimizer, my_training_dataloader
++ )
+
+  for batch in my_training_dataloader:
+      my_optimizer.zero_grad()
+      inputs, targets = batch
+-     inputs = inputs.to(device)
+-     targets = targets.to(device)
+      outputs = my_model(inputs)
+      loss = my_loss_function(outputs, targets)
+      # Just a small change for the backward instruction
+-     loss.backward()
++     accelerator.backward(loss)
+      my_optimizer.step()
+```
+
+and BOOM, your script runs on **any kind of hardware**, including CPU, multi-CPU, GPU, multi-GPU and TPU. It also supports things like [DeepSpeed](https://github.com/microsoft/DeepSpeed) and [mixed precision](https://arxiv.org/abs/1710.03740) for training efficiently.
+
+You can now run your script as follows:
+
+```bash
+accelerate config
+```
+
+=> Accelerate will ask what kind of environment you'd like to run your script on, simply answer the questions being asked. Next:
+
+```bash
+accelerate launch <your script.py>
+```
+
+This will run your script on the environment you asked for. You can always check the environment settings by typing:
+
+```bash
+accelerate env
+```
+
+You can of course change the environment by running `accelerate config` again.
+
+### 3. Create a demo
+
+Once you share a model, you then should share a [Space](https://huggingface.co/spaces) based on your SDK of choice (Gradio or Streamlit) or as a static page. 🌌
+
+![Alt text](assets/example_space.png?raw=true "Title")
+
+Here is an [example Space](https://huggingface.co/spaces/merve/anime-face-generator) corresponding to the model example shared above. Don’t know how to create a space? Read more about how to add spaces [here](https://huggingface.co/docs/hub/spaces).
+
+Below, we list some other great example GAN Spaces:
+- AnimeGANv2: https://huggingface.co/spaces/akhaliq/AnimeGANv2
+- ArcaneGAN: https://huggingface.co/spaces/akhaliq/ArcaneGAN
+- This Pokemon does not exist: https://huggingface.co/spaces/ronvolutional/ai-pokemon-card
+- GFP-GAN: https://huggingface.co/spaces/akhaliq/GFPGAN
+- DualStyleGAN: https://huggingface.co/spaces/hysts/DualStyleGAN
+
+## Example Scripts
+
+In this repo, we have provided some example scripts you can use to train your own GANs. Below is a table of the available scripts:
+
+| Name      | Paper |
+| ----------- | ----------- |
+| [DCGAN](pytorch/dcgan)  | [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://arxiv.org/abs/1511.06434)  |
+| [pix2pix](pytorch/pix2pix) | [Image-to-Image Translation with Conditional Adversarial Networks](https://arxiv.org/abs/1611.07004) |
+| [CycleGAN](pytorch/cyclegan)  | [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://arxiv.org/abs/1703.10593)
+
+## Datasets to add
+
+Below, we list some datasets which could be added to the Hub (feel free to add on one of these, or open a PR to add more datasets!):
+
+- DeepFashion: https://mmlab.ie.cuhk.edu.hk/projects/DeepFashion.html
+- Flowers: https://www.robots.ox.ac.uk/~vgg/data/flowers/
+- LSUN: https://www.yf.io/p/lsun
+
+## Links to Check Out
+
+Below, we list some possible awesome project ideas (feel free to work on one of these, or open a PR to add more project ideas!):
+
+PyTorch:
+- Lightweight-GAN: https://github.com/lucidrains/lightweight-gan
+- StyleGAN2: https://github.com/lucidrains/stylegan2-pytorch
+- StyleGAN2-ada: https://github.com/NVlabs/stylegan2-ada
+- StyleGAN3 (alias-free GAN): https://github.com/NVlabs/stylegan3
+- BigGAN: https://github.com/ajbrock/BigGAN-PyTorch, https://github.com/huggingface/pytorch-pretrained-BigGAN
+- ADGAN: https://github.com/menyifang/ADGAN
+- ICGAN: https://github.com/facebookresearch/ic_gan
+- StarGANv2: https://github.com/clovaai/stargan-v2
+- Progressive Growing GAN: https://github.com/Maggiking/PGGAN-PyTorch
+- Vision Aided GAN: https://github.com/nupurkmr9/vision-aided-gan
+- DiffAugment (for training data-efficient GANs): https://github.com/mit-han-lab/data-efficient-gans
+- StyleGAN-XL: https://github.com/autonomousvision/stylegan_xl
+- CUT: https://github.com/taesungp/contrastive-unpaired-translation
+- studioGAN (library with many GAN implementations): https://github.com/POSTECH-CVLab/PyTorch-StudioGAN
+- MMGeneration (library with many GAN implementations): https://github.com/open-mmlab/mmgeneration
+- Deformable GAN: https://github.com/ssfootball04/pose-transfer
+- Denoising Diffusion GAN: https://github.com/NVlabs/denoising-diffusion-gan
+
+Keras:
+- WGAN-GP: https://keras.io/examples/generative/wgan_gp/
+- Conditional GAN: https://keras.io/examples/generative/conditional_gan/
+- CycleGAN, DiscoGAN etc.: https://github.com/eriklindernoren/Keras-GAN
+- Neural Style Transfer: https://www.tensorflow.org/tutorials/generative/style_transfer
+- Image Super Resolution: https://github.com/idealo/image-super-resolution
+- Deformable GAN: https://github.com/AliaksandrSiarohin/pose-gan
+
+General links & tutorials:
+- https://github.com/yhlleo/GAN-Metrics
+- https://paperswithcode.com/task/image-generation
+
+## GAN metrics
+
+There have been several quantitative measures defined for assessing the quality of GANs (and other generative models). Refer to [this page](pytorch/metrics) for more info.
+
+## Evaluation
+
+For each submission, you are expected to submit:
+
+1. A model repository
+2. A space made with the model repository you created
+
+## Prizes
+
+TODO
+
+## Communication and Problems
+
+If you encounter any problems or have any questions, you should use one of the following platforms depending on your type of problem. Hugging Face is an "open-source-first" organization meaning  that we'll try to solve all problems in the most public and most transparent way possible so that everybody in the community profits.
+
+The following table summarizes what platform to use for which problem.
+
+- Problem/question/bug with the 🤗 Datasets library that you think is a general problem that also impacts other people, please open an [Issues on Datasets](https://github.com/huggingface/datasets/issues/new?assignees=&labels=bug&template=bug-report.md&title=) and ping @nielsrogge.
+- Problem/question with a modified, customized training script that is less likely to impact other people, please post your problem/question [on the forum](https://discuss.huggingface.co/) and ping @nielsrogge.
+- Other questions regarding the event, rules of the event, or if you are not sure where to post your question, please ask in the Discord channel [**#sprint-discussions**](https://discord.com/channels/879548962464493619/954111918895943720).
+
+## Talks
+
+TODO
+
+## General Tips and Tricks
+
+- Memory efficient training:
+
+In case, you are getting out-of-memory errors on your GPU, we recommend to use  [bitsandbytes](https://github.com/facebookresearch/bitsandbytes) to replace the native memory-intensive Adam optimizer with the one of `bitsandbytes`. It can be used to both train the generator and the discriminator in case you're training a GAN.
diff --git a/huggan/__init__.py b/huggan/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3f1c39e544792d8fcd640fbb4f6c0f8acab404b7
--- /dev/null
+++ b/huggan/__init__.py
@@ -0,0 +1,3 @@
+from pathlib import Path
+
+TEMPLATE_MODEL_CARD_PATH = Path(__file__).parent.absolute() / 'model_card_template.md'
\ No newline at end of file
diff --git a/huggan/assets/cyclegan.png b/huggan/assets/cyclegan.png
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+version https://git-lfs.github.com/spec/v1
+oid sha256:6ae90c6e39d2675e1059d60c4d1af1da4895eaef8665f5b3b70189b1b96d348b
+size 2743051
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+version https://git-lfs.github.com/spec/v1
+oid sha256:3f11f0a72781708bd2b2f3f9beaa87b859b501765f7782112ec758afd347dc2f
+size 2945626
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+++ b/huggan/assets/pix2pix_maps.png
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+version https://git-lfs.github.com/spec/v1
+oid sha256:ef74c7a85d56e5a4819b84bf6c1916f4b99090252f469379cf110885073b1508
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+size 1851201
diff --git a/huggan/model_card_template.md b/huggan/model_card_template.md
new file mode 100644
index 0000000000000000000000000000000000000000..5c690f3dcc978276df3d2401db29393764962d37
--- /dev/null
+++ b/huggan/model_card_template.md
@@ -0,0 +1,50 @@
+---
+tags:
+- huggan
+- gan
+# See a list of available tags here:
+# https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12
+# task: unconditional-image-generation or conditional-image-generation or image-to-image
+license: mit
+---
+
+# MyModelName
+
+## Model description
+
+Describe the model here (what it does, what it's used for, etc.)
+
+## Intended uses & limitations
+
+#### How to use
+
+```python
+# You can include sample code which will be formatted
+```
+
+#### Limitations and bias
+
+Provide examples of latent issues and potential remediations.
+
+## Training data
+
+Describe the data you used to train the model.
+If you initialized it with pre-trained weights, add a link to the pre-trained model card or repository with description of the pre-training data.
+
+## Training procedure
+
+Preprocessing, hardware used, hyperparameters...
+
+## Eval results
+
+## Generated Images
+
+You can embed local or remote images using `![](...)`
+
+### BibTeX entry and citation info
+
+```bibtex
+@inproceedings{...,
+  year={2020}
+}
+```
\ No newline at end of file
diff --git a/huggan/pytorch/README.md b/huggan/pytorch/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..d32dd1ed56a6b20d440d0af90db986fe99ccc969
--- /dev/null
+++ b/huggan/pytorch/README.md
@@ -0,0 +1,19 @@
+# Example scripts (PyTorch)
+
+This directory contains a few example scripts that allow you to train famous GANs on your own data using a bit of 🤗 magic.
+
+More concretely, these scripts:
+- leverage 🤗 [Datasets](https://huggingface.co/docs/datasets/index) to load any image dataset from the hub (including your own, possibly private, dataset)
+- leverage 🤗 [Accelerate](https://huggingface.co/docs/accelerate/index) to instantly run the script on (multi-) CPU, (multi-) GPU, TPU environments, supporting fp16 and mixed precision as well as DeepSpeed
+- leverage 🤗 [Hub](https://huggingface.co/) to push the model to the hub at the end of training, allowing to easily create a demo for it afterwards
+
+Currently, it contains the following examples:
+
+| Name      | Paper |
+| ----------- | ----------- |
+| [DCGAN](dcgan)  | [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://arxiv.org/abs/1511.06434)  |
+| [pix2pix](pix2pix) | [Image-to-Image Translation with Conditional Adversarial Networks](https://arxiv.org/abs/1611.07004) |
+| [CycleGAN](cyclegan)  | [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://arxiv.org/abs/1703.10593)
+| [Lightweight GAN](lightweight_gan) | [Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis](https://openreview.net/forum?id=1Fqg133qRaI)
+
+
diff --git a/huggan/pytorch/__init__.py b/huggan/pytorch/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/huggan/pytorch/cyclegan/README.md b/huggan/pytorch/cyclegan/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..a6c16e00d516c3ca9e5d9e43901e6d301a79c959
--- /dev/null
+++ b/huggan/pytorch/cyclegan/README.md
@@ -0,0 +1,81 @@
+# Training CycleGAN on your own data
+
+This folder contains a script to train [CycleGAN](https://arxiv.org/abs/1703.10593), leveraging the [Hugging Face](https://huggingface.co/) ecosystem for processing data and pushing the model to the Hub. 
+
+<p align="center">
+    <img src="https://camo.githubusercontent.com/16fa02525bf502bec1aac77a3eb5b96928b0f25d73f7d9dedcc041ba28c38751/68747470733a2f2f6a756e79616e7a2e6769746875622e696f2f4379636c6547414e2f696d616765732f7465617365725f686967685f7265732e6a7067" alt="drawing" width="700"/>
+</p>
+
+Example applications of CycleGAN. Taken from [this repo](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix).
+
+The script leverages 🤗 Datasets for loading and processing data, and 🤗 Accelerate for instantly running on CPU, single, multi-GPUs or TPU, also supporting mixed precision.
+
+## Launching the script
+
+To train the model with the default parameters (200 epochs, 256x256 images, etc.) on [huggan/facades](https://huggingface.co/datasets/huggan/facades) on your environment, first run:
+
+```bash
+accelerate config
+```
+
+and answer the questions asked. Next, launch the script as follows: 
+
+```
+accelerate launch train.py
+```
+
+This will create local "images" and "saved_models" directories, containing generated images and saved checkpoints over the course of the training.
+
+To train on another dataset available on the hub, simply do:
+
+```
+accelerate launch train.py --dataset huggan/edges2shoes
+```
+
+Make sure to pick a dataset which has "imageA" and "imageB" columns defined. One can always tweak the script in case the column names are different.
+
+## Training on your own data
+
+You can of course also train on your own images. For this, one can leverage Datasets' [ImageFolder](https://huggingface.co/docs/datasets/v2.0.0/en/image_process#imagefolder). Make sure to authenticate with the hub first, by running the `huggingface-cli login` command in a terminal, or the following in case you're working in a notebook:
+
+```python
+from huggingface_hub import notebook_login
+
+notebook_login()
+```
+
+Next, run the following in a notebook/script:
+
+```python
+from datasets import load_dataset
+
+# first: load dataset
+# option 1: from local folder
+dataset = load_dataset("imagefolder", data_dir="path_to_folder")
+# option 2: from remote URL (e.g. a zip file)
+dataset = load_dataset("imagefolder", data_files="URL to .zip file")
+
+# next: push to the hub (assuming git-LFS is installed)
+dataset.push_to_hub("huggan/my-awesome-dataset")
+```
+
+You can then simply pass the name of the dataset to the script:
+
+```
+accelerate launch train.py --dataset huggan/my-awesome-dataset
+```
+
+## Pushing model to the Hub
+
+You can push your trained generator to the hub after training by specifying the `push_to_hub` flag. 
+Then, you can run the script as follows:
+
+```
+accelerate launch train.py --push_to_hub --model_name cyclegan-horse2zebra
+```
+
+This is made possible by making the generator inherit from `PyTorchModelHubMixin`available in the `huggingface_hub` library. 
+
+# Citation
+
+This repo is entirely based on Erik Linder-Norén's [PyTorch-GAN repo](https://github.com/eriklindernoren/PyTorch-GAN), but with added HuggingFace goodies.
diff --git a/huggan/pytorch/cyclegan/__init__.py b/huggan/pytorch/cyclegan/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/huggan/pytorch/cyclegan/modeling_cyclegan.py b/huggan/pytorch/cyclegan/modeling_cyclegan.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b15844056313513140761ec3d42b423e68bbc30
--- /dev/null
+++ b/huggan/pytorch/cyclegan/modeling_cyclegan.py
@@ -0,0 +1,108 @@
+import torch.nn as nn
+import torch.nn.functional as F
+import torch
+
+from huggan.pytorch.huggan_mixin import HugGANModelHubMixin
+
+
+##############################
+#           RESNET
+##############################
+
+
+class ResidualBlock(nn.Module):
+    def __init__(self, in_features):
+        super(ResidualBlock, self).__init__()
+
+        self.block = nn.Sequential(
+            nn.ReflectionPad2d(1),
+            nn.Conv2d(in_features, in_features, 3),
+            nn.InstanceNorm2d(in_features),
+            nn.ReLU(inplace=True),
+            nn.ReflectionPad2d(1),
+            nn.Conv2d(in_features, in_features, 3),
+            nn.InstanceNorm2d(in_features),
+        )
+
+    def forward(self, x):
+        return x + self.block(x)
+
+
+class GeneratorResNet(nn.Module, HugGANModelHubMixin):
+    def __init__(self, input_shape, num_residual_blocks):
+        super(GeneratorResNet, self).__init__()
+
+        channels = input_shape[0]
+
+        # Initial convolution block
+        out_features = 64
+        model = [
+            nn.ReflectionPad2d(channels),
+            nn.Conv2d(channels, out_features, 7),
+            nn.InstanceNorm2d(out_features),
+            nn.ReLU(inplace=True),
+        ]
+        in_features = out_features
+
+        # Downsampling
+        for _ in range(2):
+            out_features *= 2
+            model += [
+                nn.Conv2d(in_features, out_features, 3, stride=2, padding=1),
+                nn.InstanceNorm2d(out_features),
+                nn.ReLU(inplace=True),
+            ]
+            in_features = out_features
+
+        # Residual blocks
+        for _ in range(num_residual_blocks):
+            model += [ResidualBlock(out_features)]
+
+        # Upsampling
+        for _ in range(2):
+            out_features //= 2
+            model += [
+                nn.Upsample(scale_factor=2),
+                nn.Conv2d(in_features, out_features, 3, stride=1, padding=1),
+                nn.InstanceNorm2d(out_features),
+                nn.ReLU(inplace=True),
+            ]
+            in_features = out_features
+
+        # Output layer
+        model += [nn.ReflectionPad2d(channels), nn.Conv2d(out_features, channels, 7), nn.Tanh()]
+
+        self.model = nn.Sequential(*model)
+
+    def forward(self, x):
+        return self.model(x)
+
+
+##############################
+#        Discriminator
+##############################
+
+
+class Discriminator(nn.Module):
+    def __init__(self, channels):
+        super(Discriminator, self).__init__()
+
+        def discriminator_block(in_filters, out_filters, normalize=True):
+            """Returns downsampling layers of each discriminator block"""
+            layers = [nn.Conv2d(in_filters, out_filters, 4, stride=2, padding=1)]
+            if normalize:
+                layers.append(nn.InstanceNorm2d(out_filters))
+            layers.append(nn.LeakyReLU(0.2, inplace=True))
+            return layers
+
+        self.model = nn.Sequential(
+            *discriminator_block(channels, 64, normalize=False),
+            *discriminator_block(64, 128),
+            *discriminator_block(128, 256),
+            *discriminator_block(256, 512),
+            nn.ZeroPad2d((1, 0, 1, 0)),
+            nn.Conv2d(512, 1, 4, padding=1)
+        )
+
+    def forward(self, img):
+        return self.model(img)
\ No newline at end of file
diff --git a/huggan/pytorch/cyclegan/train.py b/huggan/pytorch/cyclegan/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..3faa50efa45f10eaf8d36e725ca2d51b6c901d21
--- /dev/null
+++ b/huggan/pytorch/cyclegan/train.py
@@ -0,0 +1,354 @@
+import argparse
+import os
+import numpy as np
+import itertools
+from pathlib import Path
+import datetime
+import time
+import sys
+
+from PIL import Image
+
+from torchvision.transforms import Compose, Resize, ToTensor, Normalize, RandomCrop, RandomHorizontalFlip
+from torchvision.utils import save_image, make_grid
+
+from torch.utils.data import DataLoader
+
+from modeling_cyclegan import GeneratorResNet, Discriminator
+
+from utils import ReplayBuffer, LambdaLR
+
+from datasets import load_dataset
+
+from accelerate import Accelerator
+
+import torch.nn as nn
+import torch
+
+def parse_args(args=None):
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--epoch", type=int, default=0, help="epoch to start training from")
+    parser.add_argument("--num_epochs", type=int, default=200, help="number of epochs of training")
+    parser.add_argument("--dataset_name", type=str, default="huggan/facades", help="name of the dataset")
+    parser.add_argument("--batch_size", type=int, default=1, help="size of the batches")
+    parser.add_argument("--lr", type=float, default=0.0002, help="adam: learning rate")
+    parser.add_argument("--beta1", type=float, default=0.5, help="adam: decay of first order momentum of gradient")
+    parser.add_argument("--beta2", type=float, default=0.999, help="adam: decay of first order momentum of gradient")
+    parser.add_argument("--decay_epoch", type=int, default=100, help="epoch from which to start lr decay")
+    parser.add_argument("--num_workers", type=int, default=8, help="Number of CPU threads to use during batch generation")
+    parser.add_argument("--image_size", type=int, default=256, help="Size of images for training")
+    parser.add_argument("--channels", type=int, default=3, help="Number of image channels")
+    parser.add_argument("--sample_interval", type=int, default=100, help="interval between saving generator outputs")
+    parser.add_argument("--checkpoint_interval", type=int, default=-1, help="interval between saving model checkpoints")
+    parser.add_argument("--n_residual_blocks", type=int, default=9, help="number of residual blocks in generator")
+    parser.add_argument("--lambda_cyc", type=float, default=10.0, help="cycle loss weight")
+    parser.add_argument("--lambda_id", type=float, default=5.0, help="identity loss weight")
+    parser.add_argument("--fp16", action="store_true", help="If passed, will use FP16 training.")
+    parser.add_argument(
+        "--mixed_precision",
+        type=str,
+        default="no",
+        choices=["no", "fp16", "bf16"],
+        help="Whether to use mixed precision. Choose"
+        "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
+        "and an Nvidia Ampere GPU.",
+    )
+    parser.add_argument("--cpu", action="store_true", help="If passed, will train on the CPU.")
+    parser.add_argument(
+            "--push_to_hub",
+            action="store_true",
+            help="Whether to push the model to the HuggingFace hub after training.",
+            )
+    parser.add_argument(
+        "--pytorch_dump_folder_path",
+        required="--push_to_hub" in sys.argv,
+        type=Path,
+        help="Path to save the model. Will be created if it doesn't exist already.",
+    )
+    parser.add_argument(
+        "--model_name",
+        required="--push_to_hub" in sys.argv,
+        type=str,
+        help="Name of the model on the hub.",
+    )
+    parser.add_argument(
+        "--organization_name",
+        required=False,
+        default="huggan",
+        type=str,
+        help="Organization name to push to, in case args.push_to_hub is specified.",
+    )
+    return parser.parse_args(args=args)
+
+
+def weights_init_normal(m):
+        classname = m.__class__.__name__
+        if classname.find("Conv") != -1:
+            torch.nn.init.normal_(m.weight.data, 0.0, 0.02)
+            if hasattr(m, "bias") and m.bias is not None:
+                torch.nn.init.constant_(m.bias.data, 0.0)
+        elif classname.find("BatchNorm2d") != -1:
+            torch.nn.init.normal_(m.weight.data, 1.0, 0.02)
+            torch.nn.init.constant_(m.bias.data, 0.0)
+
+
+def training_function(config, args):
+    accelerator = Accelerator(fp16=args.fp16, cpu=args.cpu, mixed_precision=args.mixed_precision)
+    
+    # Create sample and checkpoint directories
+    os.makedirs("images/%s" % args.dataset_name, exist_ok=True)
+    os.makedirs("saved_models/%s" % args.dataset_name, exist_ok=True)
+
+    # Losses
+    criterion_GAN = torch.nn.MSELoss()
+    criterion_cycle = torch.nn.L1Loss()
+    criterion_identity = torch.nn.L1Loss()
+
+    input_shape = (args.channels, args.image_size, args.image_size)
+    # Calculate output shape of image discriminator (PatchGAN)
+    output_shape = (1, args.image_size // 2 ** 4, args.image_size // 2 ** 4)
+
+    # Initialize generator and discriminator
+    G_AB = GeneratorResNet(input_shape, args.n_residual_blocks)
+    G_BA = GeneratorResNet(input_shape, args.n_residual_blocks)
+    D_A = Discriminator(args.channels)
+    D_B = Discriminator(args.channels)
+
+    if args.epoch != 0:
+        # Load pretrained models
+        G_AB.load_state_dict(torch.load("saved_models/%s/G_AB_%d.pth" % (args.dataset_name, args.epoch)))
+        G_BA.load_state_dict(torch.load("saved_models/%s/G_BA_%d.pth" % (args.dataset_name, args.epoch)))
+        D_A.load_state_dict(torch.load("saved_models/%s/D_A_%d.pth" % (args.dataset_name, args.epoch)))
+        D_B.load_state_dict(torch.load("saved_models/%s/D_B_%d.pth" % (args.dataset_name, args.epoch)))
+    else:
+        # Initialize weights
+        G_AB.apply(weights_init_normal)
+        G_BA.apply(weights_init_normal)
+        D_A.apply(weights_init_normal)
+        D_B.apply(weights_init_normal)
+
+    # Optimizers
+    optimizer_G = torch.optim.Adam(
+        itertools.chain(G_AB.parameters(), G_BA.parameters()), lr=args.lr, betas=(args.beta1, args.beta2)
+    )
+    optimizer_D_A = torch.optim.Adam(D_A.parameters(), lr=args.lr, betas=(args.beta1, args.beta2))
+    optimizer_D_B = torch.optim.Adam(D_B.parameters(), lr=args.lr, betas=(args.beta1, args.beta2))
+
+    # Learning rate update schedulers
+    lr_scheduler_G = torch.optim.lr_scheduler.LambdaLR(
+        optimizer_G, lr_lambda=LambdaLR(args.num_epochs, args.epoch, args.decay_epoch).step
+    )
+    lr_scheduler_D_A = torch.optim.lr_scheduler.LambdaLR(
+        optimizer_D_A, lr_lambda=LambdaLR(args.num_epochs, args.epoch, args.decay_epoch).step
+    )
+    lr_scheduler_D_B = torch.optim.lr_scheduler.LambdaLR(
+        optimizer_D_B, lr_lambda=LambdaLR(args.num_epochs, args.epoch, args.decay_epoch).step
+    )
+
+    # Buffers of previously generated samples
+    fake_A_buffer = ReplayBuffer()
+    fake_B_buffer = ReplayBuffer()
+
+    # Image transformations
+    transform = Compose([
+        Resize(int(args.image_size * 1.12), Image.BICUBIC),
+        RandomCrop((args.image_size, args.image_size)),
+        RandomHorizontalFlip(),
+        ToTensor(),
+        Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
+    ])
+
+    def transforms(examples):
+        examples["A"] = [transform(image.convert("RGB")) for image in examples["imageA"]]
+        examples["B"] = [transform(image.convert("RGB")) for image in examples["imageB"]]
+
+        del examples["imageA"]
+        del examples["imageB"]
+
+        return examples
+
+    dataset = load_dataset(args.dataset_name)
+    transformed_dataset = dataset.with_transform(transforms)
+
+    splits = transformed_dataset['train'].train_test_split(test_size=0.1)
+    train_ds = splits['train']
+    val_ds = splits['test']
+
+    dataloader = DataLoader(train_ds, shuffle=True, batch_size=args.batch_size, num_workers=args.num_workers)
+    val_dataloader = DataLoader(val_ds, batch_size=5, shuffle=True, num_workers=1)
+
+    def sample_images(batches_done):
+        """Saves a generated sample from the test set"""
+        batch = next(iter(val_dataloader))
+        G_AB.eval()
+        G_BA.eval()
+        real_A = batch["A"]
+        fake_B = G_AB(real_A)
+        real_B = batch["B"]
+        fake_A = G_BA(real_B)
+        # Arange images along x-axis
+        real_A = make_grid(real_A, nrow=5, normalize=True)
+        real_B = make_grid(real_B, nrow=5, normalize=True)
+        fake_A = make_grid(fake_A, nrow=5, normalize=True)
+        fake_B = make_grid(fake_B, nrow=5, normalize=True)
+        # Arange images along y-axis
+        image_grid = torch.cat((real_A, fake_B, real_B, fake_A), 1)
+        save_image(image_grid, "images/%s/%s.png" % (args.dataset_name, batches_done), normalize=False)
+
+    G_AB, G_BA, D_A, D_B, optimizer_G, optimizer_D_A, optimizer_D_B, dataloader, val_dataloader = accelerator.prepare(G_AB, G_BA, D_A, D_B, optimizer_G, optimizer_D_A, optimizer_D_B, dataloader, val_dataloader)
+    
+    # ----------
+    #  Training
+    # ----------
+
+    prev_time = time.time()
+    for epoch in range(args.epoch, args.num_epochs):
+        for i, batch in enumerate(dataloader):
+
+            # Set model input
+            real_A = batch["A"]
+            real_B = batch["B"]
+
+            # Adversarial ground truths
+            valid = torch.ones((real_A.size(0), *output_shape), device=accelerator.device)
+            fake = torch.zeros((real_A.size(0), *output_shape), device=accelerator.device)
+
+            # ------------------
+            #  Train Generators
+            # ------------------
+
+            G_AB.train()
+            G_BA.train()
+
+            optimizer_G.zero_grad()
+
+            # Identity loss
+            loss_id_A = criterion_identity(G_BA(real_A), real_A)
+            loss_id_B = criterion_identity(G_AB(real_B), real_B)
+
+            loss_identity = (loss_id_A + loss_id_B) / 2
+
+            # GAN loss
+            fake_B = G_AB(real_A)
+            loss_GAN_AB = criterion_GAN(D_B(fake_B), valid)
+            fake_A = G_BA(real_B)
+            loss_GAN_BA = criterion_GAN(D_A(fake_A), valid)
+
+            loss_GAN = (loss_GAN_AB + loss_GAN_BA) / 2
+
+            # Cycle loss
+            recov_A = G_BA(fake_B)
+            loss_cycle_A = criterion_cycle(recov_A, real_A)
+            recov_B = G_AB(fake_A)
+            loss_cycle_B = criterion_cycle(recov_B, real_B)
+
+            loss_cycle = (loss_cycle_A + loss_cycle_B) / 2
+
+            # Total loss
+            loss_G = loss_GAN + args.lambda_cyc * loss_cycle + args.lambda_id * loss_identity
+
+            accelerator.backward(loss_G)
+            optimizer_G.step()
+
+            # -----------------------
+            #  Train Discriminator A
+            # -----------------------
+
+            optimizer_D_A.zero_grad()
+
+            # Real loss
+            loss_real = criterion_GAN(D_A(real_A), valid)
+            # Fake loss (on batch of previously generated samples)
+            fake_A_ = fake_A_buffer.push_and_pop(fake_A)
+            loss_fake = criterion_GAN(D_A(fake_A_.detach()), fake)
+            # Total loss
+            loss_D_A = (loss_real + loss_fake) / 2
+
+            accelerator.backward(loss_D_A)
+            optimizer_D_A.step()
+
+            # -----------------------
+            #  Train Discriminator B
+            # -----------------------
+
+            optimizer_D_B.zero_grad()
+
+            # Real loss
+            loss_real = criterion_GAN(D_B(real_B), valid)
+            # Fake loss (on batch of previously generated samples)
+            fake_B_ = fake_B_buffer.push_and_pop(fake_B)
+            loss_fake = criterion_GAN(D_B(fake_B_.detach()), fake)
+            # Total loss
+            loss_D_B = (loss_real + loss_fake) / 2
+
+            accelerator.backward(loss_D_B)
+            optimizer_D_B.step()
+
+            loss_D = (loss_D_A + loss_D_B) / 2
+
+            # --------------
+            #  Log Progress
+            # --------------
+
+            # Determine approximate time left
+            batches_done = epoch * len(dataloader) + i
+            batches_left = args.num_epochs * len(dataloader) - batches_done
+            time_left = datetime.timedelta(seconds=batches_left * (time.time() - prev_time))
+            prev_time = time.time()
+
+            # Print log
+            sys.stdout.write(
+                "\r[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f, adv: %f, cycle: %f, identity: %f] ETA: %s"
+                % (
+                    epoch,
+                    args.num_epochs,
+                    i,
+                    len(dataloader),
+                    loss_D.item(),
+                    loss_G.item(),
+                    loss_GAN.item(),
+                    loss_cycle.item(),
+                    loss_identity.item(),
+                    time_left,
+                )
+            )
+
+            # If at sample interval save image
+            if batches_done % args.sample_interval == 0:
+                sample_images(batches_done)
+
+        # Update learning rates
+        lr_scheduler_G.step()
+        lr_scheduler_D_A.step()
+        lr_scheduler_D_B.step()
+
+        if args.checkpoint_interval != -1 and epoch % args.checkpoint_interval == 0:
+            # Save model checkpoints
+            torch.save(G_AB.state_dict(), "saved_models/%s/G_AB_%d.pth" % (args.dataset_name, epoch))
+            torch.save(G_BA.state_dict(), "saved_models/%s/G_BA_%d.pth" % (args.dataset_name, epoch))
+            torch.save(D_A.state_dict(), "saved_models/%s/D_A_%d.pth" % (args.dataset_name, epoch))
+            torch.save(D_B.state_dict(), "saved_models/%s/D_B_%d.pth" % (args.dataset_name, epoch))
+
+    # Optionally push to hub
+    if args.push_to_hub:
+        save_directory = args.pytorch_dump_folder_path
+        if not save_directory.exists():
+            save_directory.mkdir(parents=True)
+
+        G_AB.push_to_hub(
+            repo_path_or_name=save_directory / args.model_name,
+            organization=args.organization_name,
+        )
+
+def main():
+    args = parse_args()
+    print(args)
+
+    # Make directory for saving generated images
+    os.makedirs("images", exist_ok=True)
+
+    training_function({}, args)
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/huggan/pytorch/cyclegan/utils.py b/huggan/pytorch/cyclegan/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..99874d3f4e2503266c1e829e4f84e85b642fabb8
--- /dev/null
+++ b/huggan/pytorch/cyclegan/utils.py
@@ -0,0 +1,44 @@
+import random
+import time
+import datetime
+import sys
+
+from torch.autograd import Variable
+import torch
+import numpy as np
+
+from torchvision.utils import save_image
+
+
+class ReplayBuffer:
+    def __init__(self, max_size=50):
+        assert max_size > 0, "Empty buffer or trying to create a black hole. Be careful."
+        self.max_size = max_size
+        self.data = []
+
+    def push_and_pop(self, data):
+        to_return = []
+        for element in data.data:
+            element = torch.unsqueeze(element, 0)
+            if len(self.data) < self.max_size:
+                self.data.append(element)
+                to_return.append(element)
+            else:
+                if random.uniform(0, 1) > 0.5:
+                    i = random.randint(0, self.max_size - 1)
+                    to_return.append(self.data[i].clone())
+                    self.data[i] = element
+                else:
+                    to_return.append(element)
+        return Variable(torch.cat(to_return))
+
+
+class LambdaLR:
+    def __init__(self, n_epochs, offset, decay_start_epoch):
+        assert (n_epochs - decay_start_epoch) > 0, "Decay must start before the training session ends!"
+        self.n_epochs = n_epochs
+        self.offset = offset
+        self.decay_start_epoch = decay_start_epoch
+
+    def step(self, epoch):
+        return 1.0 - max(0, epoch + self.offset - self.decay_start_epoch) / (self.n_epochs - self.decay_start_epoch)
\ No newline at end of file
diff --git a/huggan/pytorch/dcgan/README.md b/huggan/pytorch/dcgan/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..515da8f33a30d223bf9d6af385e804a07756dbd4
--- /dev/null
+++ b/huggan/pytorch/dcgan/README.md
@@ -0,0 +1,155 @@
+# Train DCGAN on your custom data
+
+This folder contains a script to train [DCGAN](https://arxiv.org/abs/1511.06434) for unconditional image generation, leveraging the [Hugging Face](https://huggingface.co/) ecosystem for processing your data and pushing the model to the Hub.
+
+The script leverages 🤗 Datasets for loading and processing data, and 🤗 Accelerate for instantly running on CPU, single, multi-GPUs or TPU, also supporting fp16/mixed precision.
+
+<p align="center">
+    <img src="https://raw.githubusercontent.com/huggingface/community-events/main/huggan/assets/dcgan_mnist.png" alt="drawing" width="300"/>
+</p>
+
+
+## Launching the script
+
+To train the model with the default parameters (5 epochs, 64x64 images, etc.) on [MNIST](https://huggingface.co/datasets/mnist), first run:
+
+```bash
+accelerate config
+```
+
+and answer the questions asked about your environment. Next, launch the script as follows: 
+
+```bash
+accelerate launch train.py
+```
+
+This will create a local "images" directory, containing generated images over the course of the training.
+
+To train on another dataset available on the hub, simply do (for instance):
+
+```bash
+python train.py --dataset cifar-10
+```
+
+In case you'd like to tweak the script to your liking, first fork the "community-events" [repo](https://github.com/huggingface/community-events) (see the button on the top right), then clone it locally:
+
+```bash
+git clone https://github.com/<your Github username>/community-events.git
+```
+
+and edit to your liking.
+
+## Training on your own data
+
+You can of course also train on your own images. For this, one can leverage Datasets' [ImageFolder](https://huggingface.co/docs/datasets/v2.0.0/en/image_process#imagefolder). Make sure to authenticate with the hub first, by running the `huggingface-cli login` command in a terminal, or the following in case you're working in a notebook:
+
+```python
+from huggingface_hub import notebook_login
+
+notebook_login()
+```
+
+Next, run the following in a notebook/script:
+
+```python
+from datasets import load_dataset
+
+# first: load dataset
+# option 1: from local folder
+dataset = load_dataset("imagefolder", data_dir="path_to_folder")
+# option 2: from remote URL (e.g. a zip file)
+dataset = load_dataset("imagefolder", data_files="URL to .zip file")
+
+# next: push to the hub (assuming git-LFS is installed)
+dataset.push_to_hub("huggan/my-awesome-dataset")
+```
+
+You can then simply pass the name of the dataset to the script:
+
+```bash
+accelerate launch train.py --dataset huggan/my-awesome-dataset
+```
+
+## Pushing model to the Hub
+
+You can push your trained generator to the hub after training by specifying the `push_to_hub` flag, along with a `model_name` and `pytorch_dump_folder_path`. 
+
+```bash
+accelerate launch train.py --push_to_hub --model_name dcgan-mnist
+```
+
+This is made possible by making the generator inherit from `PyTorchModelHubMixin`available in the `huggingface_hub` library. 
+
+This means that after training, generating a new image can be done as follows:
+
+```python
+import torch
+import torch.nn as nn
+from torchvision.transforms import ToPILImage
+from huggingface_hub import PyTorchModelHubMixin
+
+class Generator(nn.Module, PyTorchModelHubMixin):
+    def __init__(self, num_channels=3, latent_dim=100, hidden_size=64):
+        super(Generator, self).__init__()
+        self.model = nn.Sequential(
+            # input is Z, going into a convolution
+            nn.ConvTranspose2d(latent_dim, hidden_size * 8, 4, 1, 0, bias=False),
+            nn.BatchNorm2d(hidden_size * 8),
+            nn.ReLU(True),
+            # state size. (hidden_size*8) x 4 x 4
+            nn.ConvTranspose2d(hidden_size * 8, hidden_size * 4, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(hidden_size * 4),
+            nn.ReLU(True),
+            # state size. (hidden_size*4) x 8 x 8
+            nn.ConvTranspose2d(hidden_size * 4, hidden_size * 2, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(hidden_size * 2),
+            nn.ReLU(True),
+            # state size. (hidden_size*2) x 16 x 16
+            nn.ConvTranspose2d(hidden_size * 2, hidden_size, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(hidden_size),
+            nn.ReLU(True),
+            # state size. (hidden_size) x 32 x 32
+            nn.ConvTranspose2d(hidden_size, num_channels, 4, 2, 1, bias=False),
+            nn.Tanh()
+            # state size. (num_channels) x 64 x 64
+        )
+
+    def forward(self, noise):
+        pixel_values = self.model(noise)
+
+        return pixel_values
+
+model = Generator.from_pretrained("huggan/dcgan-mnist")
+
+device = "cuda" if torch.cuda.is_available() else "cpu
+model.to(device)
+ 
+with torch.no_grad():
+    z = torch.randn(1, 100, 1, 1, device=device)
+    pixel_values = model(z)
+
+# turn into actual image
+image = pixel_values[0]
+image = (image + 1) /2
+image = ToPILImage()(image)
+image.save("generated.png")
+```
+
+## Weights and Biases integration
+
+You can easily add logging to [Weights and Biases](https://wandb.ai/site) by passing the `--wandb` flag:
+
+```bash
+accelerate launch train.py --wandb
+````
+
+You can then follow the progress of your GAN in a browser:
+
+<p align="center">
+    <img src="https://raw.githubusercontent.com/huggingface/community-events/main/huggan/assets/wandb.png" alt="drawing" width="700"/>
+</p>
+
+
+# Citation
+
+This repo is entirely based on PyTorch's official [DCGAN tutorial](https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html), but with added HuggingFace goodies.
diff --git a/huggan/pytorch/dcgan/__init__.py b/huggan/pytorch/dcgan/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/huggan/pytorch/dcgan/modeling_dcgan.py b/huggan/pytorch/dcgan/modeling_dcgan.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f1bf88207f15723d3e98bdeface109b368ea987
--- /dev/null
+++ b/huggan/pytorch/dcgan/modeling_dcgan.py
@@ -0,0 +1,80 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright (c) 2022 PyTorch contributors and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions.
+
+import torch.nn as nn
+
+from huggan.pytorch.huggan_mixin import HugGANModelHubMixin
+
+
+class Generator(nn.Module, HugGANModelHubMixin):
+    def __init__(self, num_channels=3, latent_dim=100, hidden_size=64):
+        super(Generator, self).__init__()
+        self.model = nn.Sequential(
+            # input is Z, going into a convolution
+            nn.ConvTranspose2d(latent_dim, hidden_size * 8, 4, 1, 0, bias=False),
+            nn.BatchNorm2d(hidden_size * 8),
+            nn.ReLU(True),
+            # state size. (hidden_size*8) x 4 x 4
+            nn.ConvTranspose2d(hidden_size * 8, hidden_size * 4, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(hidden_size * 4),
+            nn.ReLU(True),
+            # state size. (hidden_size*4) x 8 x 8
+            nn.ConvTranspose2d(hidden_size * 4, hidden_size * 2, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(hidden_size * 2),
+            nn.ReLU(True),
+            # state size. (hidden_size*2) x 16 x 16
+            nn.ConvTranspose2d(hidden_size * 2, hidden_size, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(hidden_size),
+            nn.ReLU(True),
+            # state size. (hidden_size) x 32 x 32
+            nn.ConvTranspose2d(hidden_size, num_channels, 4, 2, 1, bias=False),
+            nn.Tanh()
+            # state size. (num_channels) x 64 x 64
+        )
+
+    def forward(self, noise):
+        pixel_values = self.model(noise)
+
+        return pixel_values
+
+
+class Discriminator(nn.Module):
+    def __init__(self, num_channels=3, hidden_size=64):
+        super(Discriminator, self).__init__()
+        self.model = nn.Sequential(
+            # input is (num_channels) x 64 x 64
+            nn.Conv2d(num_channels, hidden_size, 4, 2, 1, bias=False),
+            nn.LeakyReLU(0.2, inplace=True),
+            # state size. (hidden_size) x 32 x 32
+            nn.Conv2d(hidden_size, hidden_size * 2, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(hidden_size * 2),
+            nn.LeakyReLU(0.2, inplace=True),
+            # state size. (hidden_size*2) x 16 x 16
+            nn.Conv2d(hidden_size * 2, hidden_size * 4, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(hidden_size * 4),
+            nn.LeakyReLU(0.2, inplace=True),
+            # state size. (hidden_size*4) x 8 x 8
+            nn.Conv2d(hidden_size * 4, hidden_size * 8, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(hidden_size * 8),
+            nn.LeakyReLU(0.2, inplace=True),
+            # state size. (hidden_size*8) x 4 x 4
+            nn.Conv2d(hidden_size * 8, 1, 4, 1, 0, bias=False),
+            nn.Sigmoid(),
+        )
+
+    def forward(self, pixel_values):
+        logits = self.model(pixel_values)
+
+        return logits
diff --git a/huggan/pytorch/dcgan/train.py b/huggan/pytorch/dcgan/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..6daa1741dc65f982164a83271b50c624d6a22751
--- /dev/null
+++ b/huggan/pytorch/dcgan/train.py
@@ -0,0 +1,346 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright (c) 2022 PyTorch contributors and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions.
+
+""" Training a Deep Convolutional Generative Adversarial Network (DCGAN) leveraging the 🤗 ecosystem.
+Paper: https://arxiv.org/abs/1511.06434.
+Based on PyTorch's official tutorial: https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html.
+"""
+
+
+import argparse
+import logging
+import os
+import sys
+from pathlib import Path
+
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader
+from torchvision.transforms import (CenterCrop, Compose, Normalize, Resize,
+                                    ToTensor, ToPILImage)
+from torchvision.utils import save_image
+
+from PIL import Image, ImageFile
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+
+from accelerate import Accelerator
+
+from modeling_dcgan import Discriminator, Generator
+
+from datasets import load_dataset
+
+from huggan.pytorch.metrics.inception import InceptionV3
+from huggan.pytorch.metrics.fid_score import calculate_fretchet
+
+import wandb
+
+logger = logging.getLogger(__name__)
+
+
+def parse_args(args=None):
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--dataset", type=str, default="mnist", help="Dataset to load from the HuggingFace hub.")
+    parser.add_argument("--num_workers", type=int, default=0, help="Number of workers when loading data")
+    parser.add_argument("--batch_size", type=int, default=128, help="Batch size to use during training")
+    parser.add_argument(
+        "--image_size",
+        type=int,
+        default=64,
+        help="Spatial size to use when resizing images for training.",
+    )
+    parser.add_argument(
+        "--num_channels",
+        type=int,
+        default=3,
+        help="Number of channels in the training images. For color images this is 3.",
+    )
+    parser.add_argument("--latent_dim", type=int, default=100, help="Dimensionality of the latent space.")
+    parser.add_argument(
+        "--generator_hidden_size",
+        type=int,
+        default=64,
+        help="Hidden size of the generator's feature maps.",
+    )
+    parser.add_argument(
+        "--discriminator_hidden_size",
+        type=int,
+        default=64,
+        help="Hidden size of the discriminator's feature maps.",
+    )
+    parser.add_argument("--num_epochs", type=int, default=5, help="number of epochs of training")
+    parser.add_argument("--lr", type=float, default=0.0002, help="adam: learning rate")
+    parser.add_argument(
+        "--beta1",
+        type=float,
+        default=0.5,
+        help="adam: decay of first order momentum of gradient",
+    )
+    parser.add_argument("--fp16", action="store_true", help="If passed, will use FP16 training.")
+    parser.add_argument(
+        "--mixed_precision",
+        type=str,
+        default="no",
+        choices=["no", "fp16", "bf16"],
+        help="Whether to use mixed precision. Choose"
+        "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
+        "and an Nvidia Ampere GPU.",
+    )
+    parser.add_argument("--cpu", action="store_true", help="If passed, will train on the CPU.")
+    parser.add_argument("--output_dir", type=Path, default=Path("./output"), help="Name of the directory to dump generated images during training.")
+    parser.add_argument("--wandb", action="store_true", help="If passed, will log to Weights and Biases.")
+    parser.add_argument(
+        "--logging_steps",
+        type=int,
+        default=50,
+        help="Number of steps between each logging",
+    )
+    parser.add_argument(
+        "--push_to_hub",
+        action="store_true",
+        help="Whether to push the model to the HuggingFace hub after training.",
+        )
+    parser.add_argument(
+        "--model_name",
+        default=None,
+        type=str,
+        help="Name of the model on the hub.",
+    )
+    parser.add_argument(
+        "--organization_name",
+        default="huggan",
+        type=str,
+        help="Organization name to push to, in case args.push_to_hub is specified.",
+    )
+    args = parser.parse_args()
+    
+    if args.push_to_hub:
+        assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed."
+        assert args.model_name is not None, "Need a `model_name` to create a repo when `--push_to_hub` is passed."
+
+    if args.output_dir is not None:
+        os.makedirs(args.output_dir, exist_ok=True)
+    
+    return args
+
+
+# Custom weights initialization called on Generator and Discriminator
+def weights_init(m):
+    classname = m.__class__.__name__
+    if classname.find("Conv") != -1:
+        nn.init.normal_(m.weight.data, 0.0, 0.02)
+    elif classname.find("BatchNorm") != -1:
+        nn.init.normal_(m.weight.data, 1.0, 0.02)
+        nn.init.constant_(m.bias.data, 0)
+
+
+def training_function(config, args):
+
+    # Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
+    accelerator = Accelerator(fp16=args.fp16, cpu=args.cpu, mixed_precision=args.mixed_precision)
+
+    # Setup logging, we only want one process per machine to log things on the screen.
+    # accelerator.is_local_main_process is only True for one process per machine.
+    logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
+    if accelerator.is_local_main_process:
+        # set up Weights and Biases if requested
+        if args.wandb:
+            import wandb
+
+            wandb.init(project=str(args.output_dir).split("/")[-1])   
+    
+    # Loss function
+    criterion = nn.BCELoss()
+
+    # Initialize generator and discriminator
+    generator = Generator(
+        num_channels=args.num_channels,
+        latent_dim=args.latent_dim,
+        hidden_size=args.generator_hidden_size,
+    )
+    discriminator = Discriminator(num_channels=args.num_channels, hidden_size=args.discriminator_hidden_size)
+
+    # Initialize weights
+    generator.apply(weights_init)
+    discriminator.apply(weights_init)
+
+    # Initialize Inceptionv3 (for FID metric)
+    model = InceptionV3()
+
+    # Initialize Inceptionv3 (for FID metric)
+    model = InceptionV3()
+
+    # Create batch of latent vectors that we will use to visualize
+    # the progression of the generator
+    fixed_noise = torch.randn(64, args.latent_dim, 1, 1, device=accelerator.device)
+
+    # Establish convention for real and fake labels during training
+    real_label = 1.0
+    fake_label = 0.0
+
+    # Setup Adam optimizers for both G and D
+    discriminator_optimizer = torch.optim.Adam(discriminator.parameters(), lr=args.lr, betas=(args.beta1, 0.999))
+    generator_optimizer = torch.optim.Adam(generator.parameters(), lr=args.lr, betas=(args.beta1, 0.999))
+
+    # Configure data loader
+    dataset = load_dataset(args.dataset)
+
+    transform = Compose(
+        [
+            Resize(args.image_size),
+            CenterCrop(args.image_size),
+            ToTensor(),
+            Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
+        ]
+    )
+
+    def transforms(examples):
+        examples["pixel_values"] = [transform(image.convert("RGB")) for image in examples["image"]]
+
+        del examples["image"]
+
+        return examples
+
+    transformed_dataset = dataset.with_transform(transforms)
+
+    dataloader = DataLoader(
+        transformed_dataset["train"], batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers
+    )
+
+    generator, discriminator, generator_optimizer, discriminator_optimizer, dataloader = accelerator.prepare(generator, discriminator, generator_optimizer, discriminator_optimizer, dataloader)
+
+    # ----------
+    #  Training
+    # ----------
+    
+    # Training Loop
+
+    # Lists to keep track of progress
+    img_list = []
+
+    logger.info("***** Running training *****")
+    logger.info(f"  Num Epochs = {args.num_epochs}")
+    # For each epoch
+    for epoch in range(args.num_epochs):
+        # For each batch in the dataloader
+        for step, batch in enumerate(dataloader, 0):
+
+            ############################
+            # (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))
+            ###########################
+            ## Train with all-real batch
+            discriminator.zero_grad()
+            # Format batch
+            real_cpu = batch["pixel_values"]
+            batch_size = real_cpu.size(0)
+            label = torch.full((batch_size,), real_label, dtype=torch.float, device=accelerator.device)
+            # Forward pass real batch through D
+            output = discriminator(real_cpu).view(-1)
+            # Calculate loss on all-real batch
+            errD_real = criterion(output, label)
+            # Calculate gradients for D in backward pass
+            accelerator.backward(errD_real)
+            D_x = output.mean().item()
+
+            ## Train with all-fake batch
+            # Generate batch of latent vectors
+            noise = torch.randn(batch_size, args.latent_dim, 1, 1, device=accelerator.device)
+            # Generate fake image batch with G
+            fake = generator(noise)
+            label.fill_(fake_label)
+            # Classify all fake batch with D
+            output = discriminator(fake.detach()).view(-1)
+            # Calculate D's loss on the all-fake batch
+            errD_fake = criterion(output, label)
+            # Calculate the gradients for this batch, accumulated (summed) with previous gradients
+            accelerator.backward(errD_fake)
+            D_G_z1 = output.mean().item()
+            # Compute error of D as sum over the fake and the real batches
+            errD = errD_real + errD_fake
+            # Update D
+            discriminator_optimizer.step()
+
+            ############################
+            # (2) Update G network: maximize log(D(G(z)))
+            ###########################
+            generator.zero_grad()
+            label.fill_(real_label)  # fake labels are real for generator cost
+            # Since we just updated D, perform another forward pass of all-fake batch through D
+            output = discriminator(fake).view(-1)
+            # Calculate G's loss based on this output
+            errG = criterion(output, label)
+            # Calculate gradients for G
+            accelerator.backward(errG)
+            D_G_z2 = output.mean().item()
+            # Update G
+            generator_optimizer.step()
+
+            # Log all results
+            if (step + 1) % args.logging_steps == 0:
+                errD.detach()
+                errG.detach()
+
+                if accelerator.state.num_processes > 1:
+                    errD = accelerator.gather(errD).sum() / accelerator.state.num_processes
+                    errG = accelerator.gather(errG).sum() / accelerator.state.num_processes
+
+                    train_logs = {
+                        "epoch": epoch,
+                        "discriminator_loss": errD,
+                        "generator_loss": errG,
+                        "D_x": D_x,
+                        "D_G_z1": D_G_z1,
+                        "D_G_z2": D_G_z2,
+                    }
+                    log_str = ""
+                    for k, v in train_logs.items():
+                        log_str += "| {}: {:.3e}".format(k, v)
+
+                    if accelerator.is_local_main_process:
+                        logger.info(log_str)
+                        if args.wandb:
+                            wandb.log(train_logs)
+
+            # Check how the generator is doing by saving G's output on fixed_noise
+            if (step % 500 == 0) or ((epoch == args.num_epochs - 1) and (step == len(dataloader) - 1)):
+                with torch.no_grad():
+                    fake_images = generator(fixed_noise).detach().cpu()
+                file_name = args.output_dir/f"iter_{step}.png"
+                save_image(fake_images.data[:25], file_name, nrow=5, normalize=True)
+                if accelerator.is_local_main_process and args.wandb:
+                    wandb.log({'generated_examples': wandb.Image(str(file_name)) })
+
+        # Calculate FID metric
+        fid = calculate_fretchet(real_cpu, fake, model.to(accelerator.device))
+        logger.info(f"FID: {fid}")
+        if accelerator.is_local_main_process and args.wandb:
+            wandb.log({"FID": fid})
+
+    # Optionally push to hub
+    if accelerator.is_main_process and args.push_to_hub:
+        generator.module.push_to_hub(
+            repo_path_or_name=args.output_dir / args.model_name,
+            organization=args.organization_name,
+        )
+
+
+def main():
+    args = parse_args()
+    print(args)
+
+    training_function({}, args)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/huggan/pytorch/huggan_mixin.py b/huggan/pytorch/huggan_mixin.py
new file mode 100644
index 0000000000000000000000000000000000000000..2267d75af305052534fb6357bf23258c75168f80
--- /dev/null
+++ b/huggan/pytorch/huggan_mixin.py
@@ -0,0 +1,131 @@
+from pathlib import Path
+from re import TEMPLATE
+from typing import Optional, Union
+import os
+
+from huggingface_hub import PyTorchModelHubMixin, HfApi, HfFolder, Repository
+
+from huggan import TEMPLATE_MODEL_CARD_PATH
+
+
+class HugGANModelHubMixin(PyTorchModelHubMixin):
+    """A mixin to push PyTorch Models to the Hugging Face Hub. This
+    mixin was adapted from the PyTorchModelHubMixin to also push a template
+    README.md for the HugGAN sprint.
+    """
+
+    def push_to_hub(
+        self,
+        repo_path_or_name: Optional[str] = None,
+        repo_url: Optional[str] = None,
+        commit_message: Optional[str] = "Add model",
+        organization: Optional[str] = None,
+        private: Optional[bool] = None,
+        api_endpoint: Optional[str] = None,
+        use_auth_token: Optional[Union[bool, str]] = None,
+        git_user: Optional[str] = None,
+        git_email: Optional[str] = None,
+        config: Optional[dict] = None,
+        skip_lfs_files: bool = False,
+        default_model_card: Optional[str] = TEMPLATE_MODEL_CARD_PATH
+    ) -> str:
+        """
+        Upload model checkpoint or tokenizer files to the Hub while
+        synchronizing a local clone of the repo in `repo_path_or_name`.
+        Parameters:
+            repo_path_or_name (`str`, *optional*):
+                Can either be a repository name for your model or tokenizer in
+                the Hub or a path to a local folder (in which case the
+                repository will have the name of that local folder). If not
+                specified, will default to the name given by `repo_url` and a
+                local directory with that name will be created.
+            repo_url (`str`, *optional*):
+                Specify this in case you want to push to an existing repository
+                in the hub. If unspecified, a new repository will be created in
+                your namespace (unless you specify an `organization`) with
+                `repo_name`.
+            commit_message (`str`, *optional*):
+                Message to commit while pushing. Will default to `"add config"`,
+                `"add tokenizer"` or `"add model"` depending on the type of the
+                class.
+            organization (`str`, *optional*):
+                Organization in which you want to push your model or tokenizer
+                (you must be a member of this organization).
+            private (`bool`, *optional*):
+                Whether the repository created should be private.
+            api_endpoint (`str`, *optional*):
+                The API endpoint to use when pushing the model to the hub.
+            use_auth_token (`bool` or `str`, *optional*):
+                The token to use as HTTP bearer authorization for remote files.
+                If `True`, will use the token generated when running
+                `transformers-cli login` (stored in `~/.huggingface`). Will
+                default to `True` if `repo_url` is not specified.
+            git_user (`str`, *optional*):
+                will override the `git config user.name` for committing and
+                pushing files to the hub.
+            git_email (`str`, *optional*):
+                will override the `git config user.email` for committing and
+                pushing files to the hub.
+            config (`dict`, *optional*):
+                Configuration object to be saved alongside the model weights.
+            default_model_card (`str`, *optional*):
+                Path to a markdown file to use as your default model card.
+        Returns:
+            The url of the commit of your model in the given repository.
+        """
+
+        if repo_path_or_name is None and repo_url is None:
+            raise ValueError(
+                "You need to specify a `repo_path_or_name` or a `repo_url`."
+            )
+
+        if use_auth_token is None and repo_url is None:
+            token = HfFolder.get_token()
+            if token is None:
+                raise ValueError(
+                    "You must login to the Hugging Face hub on this computer by typing `huggingface-cli login` and "
+                    "entering your credentials to use `use_auth_token=True`. Alternatively, you can pass your own "
+                    "token as the `use_auth_token` argument."
+                )
+        elif isinstance(use_auth_token, str):
+            token = use_auth_token
+        else:
+            token = None
+
+        if repo_path_or_name is None:
+            repo_path_or_name = repo_url.split("/")[-1]
+
+        # If no URL is passed and there's no path to a directory containing files, create a repo
+        if repo_url is None and not os.path.exists(repo_path_or_name):
+            repo_id = Path(repo_path_or_name).name
+            if organization:
+                repo_id = f"{organization}/{repo_id}"
+            repo_url = HfApi(endpoint=api_endpoint).create_repo(
+                repo_id=repo_id,
+                token=token,
+                private=private,
+                repo_type=None,
+                exist_ok=True,
+            )
+
+        repo = Repository(
+            repo_path_or_name,
+            clone_from=repo_url,
+            use_auth_token=use_auth_token,
+            git_user=git_user,
+            git_email=git_email,
+            skip_lfs_files=skip_lfs_files
+        )
+        repo.git_pull(rebase=True)
+
+        # Save the files in the cloned repo
+        self.save_pretrained(repo_path_or_name, config=config)
+
+        model_card_path = Path(repo_path_or_name) / 'README.md'
+        if not model_card_path.exists():
+            model_card_path.write_text(TEMPLATE_MODEL_CARD_PATH.read_text())
+
+        # Commit and push!
+        repo.git_add()
+        repo.git_commit(commit_message)
+        return repo.git_push()
diff --git a/huggan/pytorch/lightweight_gan/README.md b/huggan/pytorch/lightweight_gan/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..8f5c49d433dca56a420478e0f4a005bc7404a412
--- /dev/null
+++ b/huggan/pytorch/lightweight_gan/README.md
@@ -0,0 +1,89 @@
+# Train Lightweight GAN on your custom data
+
+This folder contains a script to train ['Lightweight' GAN](https://openreview.net/forum?id=1Fqg133qRaI) for unconditional image generation, leveraging the [Hugging Face](https://huggingface.co/) ecosystem for processing your data and pushing the model to the Hub.
+
+The script leverages 🤗 Datasets for loading and processing data, and 🤗 Accelerate for instantly running on CPU, single, multi-GPUs or TPU, also supporting mixed precision.
+
+<p align="center">
+    <img src="https://raw.githubusercontent.com/lucidrains/lightweight-gan/main/images/pizza-512.jpg" alt="drawing" width="300"/>
+</p>
+
+Pizza's that don't exist. Courtesy of Phil Wang.
+
+## Launching the script
+
+To train the model with the default parameters on [huggan/CelebA-faces](https://huggingface.co/datasets/huggan/CelebA-faces), first run:
+
+```bash
+accelerate config
+```
+
+and answer the questions asked about your environment. Next, launch the script as follows: 
+
+```bash
+accelerate launch cli.py
+```
+
+This will instantly run on multi-GPUs (if you asked for that). To train on another dataset available on the hub, simply do (for instance):
+
+```bash
+accelerate launch cli.py --dataset_name huggan/pokemon
+```
+
+In case you'd like to tweak the script to your liking, first fork the "community-events" [repo](https://github.com/huggingface/community-events) (see the button on the top right), then clone it locally:
+
+```bash
+git clone https://github.com/<your Github username>/community-events.git
+```
+
+and edit to your liking.
+
+## Training on your own data
+
+You can of course also train on your own images. For this, one can leverage Datasets' [ImageFolder](https://huggingface.co/docs/datasets/v2.0.0/en/image_process#imagefolder). Make sure to authenticate with the hub first, by running the `huggingface-cli login` command in a terminal, or the following in case you're working in a notebook:
+
+```python
+from huggingface_hub import notebook_login
+
+notebook_login()
+```
+
+Next, run the following in a notebook/script:
+
+```python
+from datasets import load_dataset
+
+# first: load dataset
+# option 1: from local folder
+dataset = load_dataset("imagefolder", data_dir="path_to_folder")
+# option 2: from remote URL (e.g. a zip file)
+dataset = load_dataset("imagefolder", data_files="URL to .zip file")
+
+# next: push to the hub (assuming git-LFS is installed)
+dataset.push_to_hub("huggan/my-awesome-dataset")
+```
+
+You can then simply pass the name of the dataset to the script:
+
+```bash
+accelerate launch cli.py --dataset huggan/my-awesome-dataset
+```
+
+## Weights and Biases integration
+
+You can easily add logging to [Weights and Biases](https://wandb.ai/site) by passing the `--wandb` flag:
+
+```bash
+accelerate launch cli.py --wandb
+````
+
+You can then follow the progress of your GAN in a browser:
+
+<p align="center">
+    <img src="https://raw.githubusercontent.com/huggingface/community-events/main/huggan/assets/lightweight_gan_wandb.png" alt="drawing" width="700"/>
+</p>
+
+
+# Citation
+
+This repo is entirely based on lucidrains' [Pytorch implementation](https://github.com/lucidrains/lightweight-gan), but with added HuggingFace goodies.
diff --git a/huggan/pytorch/lightweight_gan/__init__.py b/huggan/pytorch/lightweight_gan/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/huggan/pytorch/lightweight_gan/cli.py b/huggan/pytorch/lightweight_gan/cli.py
new file mode 100644
index 0000000000000000000000000000000000000000..056391613737488200c5d246eff4fcabf3ea1b31
--- /dev/null
+++ b/huggan/pytorch/lightweight_gan/cli.py
@@ -0,0 +1,178 @@
+import fire
+import random
+from retry.api import retry_call
+from tqdm import tqdm
+from datetime import datetime
+from pathlib import Path
+from lightweight_gan import Trainer, NanException
+
+import torch
+import torch.multiprocessing as mp
+
+import numpy as np
+
+def exists(val):
+    return val is not None
+
+def default(val, d):
+    return val if exists(val) else d
+
+def cast_list(el):
+    return el if isinstance(el, list) else [el]
+
+def timestamped_filename(prefix = 'generated-'):
+    now = datetime.now()
+    timestamp = now.strftime("%m-%d-%Y_%H-%M-%S")
+    return f'{prefix}{timestamp}'
+
+def set_seed(seed):
+    torch.manual_seed(seed)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+    np.random.seed(seed)
+    random.seed(seed)
+
+def run_training(model_args, data, load_from, new, num_train_steps, name, seed):
+    
+    if seed is not None:
+        set_seed(seed)
+
+    model = Trainer(**model_args)
+
+    if not new:
+        model.load(load_from)
+    else:
+        model.clear()
+
+    progress_bar = tqdm(initial = model.steps, total = num_train_steps, mininterval=10., desc=f'{name}<{data}>')
+    G, D, D_aug = model.init_accelerator()
+
+    # model.set_data_src(data)
+
+    while model.steps < num_train_steps:
+        # retry_call(model.train, tries=3, exceptions=NanException)
+        model.train(G, D, D_aug)
+        progress_bar.n = model.steps
+        progress_bar.refresh()
+        if model.accelerator.is_local_main_process and model.steps % 50 == 0:
+            model.print_log()
+
+    model.save(model.checkpoint_num)
+
+def train_from_folder(
+    dataset_name = 'huggan/CelebA-faces',
+    data = './data',
+    results_dir = './results',
+    models_dir = './models',
+    name = 'default',
+    new = False,
+    load_from = -1,
+    image_size = 256,
+    optimizer = 'adam',
+    fmap_max = 512,
+    transparent = False,
+    greyscale = False,
+    batch_size = 10,
+    gradient_accumulate_every = 4,
+    num_train_steps = 150000,
+    learning_rate = 2e-4,
+    save_every = 10000,
+    evaluate_every = 1000,
+    generate = False,
+    generate_types = ['default', 'ema'],
+    generate_interpolation = False,
+    aug_test = False,
+    aug_prob=None,
+    aug_types=['cutout', 'translation'],
+    dataset_aug_prob=0.,
+    attn_res_layers = [32],
+    freq_chan_attn = False,
+    disc_output_size = 1,
+    dual_contrast_loss = False,
+    antialias = False,
+    interpolation_num_steps = 100,
+    save_frames = False,
+    num_image_tiles = None,
+    calculate_fid_every = None,
+    calculate_fid_num_images = 12800,
+    clear_fid_cache = False,
+    seed = 42,
+    cpu = False,
+    mixed_precision = "no",
+    show_progress = False,
+    wandb = False,
+    push_to_hub = False,
+    organization_name = None,
+):
+    if push_to_hub:
+        if name == 'default':
+            raise RuntimeError(
+                "You've chosen to push to hub, but have left the --name flag as 'default'."
+                " You should name your model something other than 'default'!"
+            )
+
+    num_image_tiles = default(num_image_tiles, 4 if image_size > 512 else 8)
+
+    model_args = dict(
+        dataset_name = dataset_name,
+        name = name,
+        results_dir = results_dir,
+        models_dir = models_dir,
+        batch_size = batch_size,
+        gradient_accumulate_every = gradient_accumulate_every,
+        attn_res_layers = cast_list(attn_res_layers),
+        freq_chan_attn = freq_chan_attn,
+        disc_output_size = disc_output_size,
+        dual_contrast_loss = dual_contrast_loss,
+        antialias = antialias,
+        image_size = image_size,
+        num_image_tiles = num_image_tiles,
+        optimizer = optimizer,
+        fmap_max = fmap_max,
+        transparent = transparent,
+        greyscale = greyscale,
+        lr = learning_rate,
+        save_every = save_every,
+        evaluate_every = evaluate_every,
+        aug_prob = aug_prob,
+        aug_types = cast_list(aug_types),
+        dataset_aug_prob = dataset_aug_prob,
+        calculate_fid_every = calculate_fid_every,
+        calculate_fid_num_images = calculate_fid_num_images,
+        clear_fid_cache = clear_fid_cache,
+        cpu = cpu,
+        mixed_precision = mixed_precision,
+        wandb = wandb,
+        push_to_hub = push_to_hub,
+        organization_name = organization_name
+    )
+
+    if generate:
+        model = Trainer(**model_args)
+        model.load(load_from)
+        samples_name = timestamped_filename()
+        checkpoint = model.checkpoint_num
+        dir_result = model.generate(samples_name, num_image_tiles, checkpoint, generate_types)
+        print(f'sample images generated at {dir_result}')
+        return
+
+    if generate_interpolation:
+        model = Trainer(**model_args)
+        model.load(load_from)
+        samples_name = timestamped_filename()
+        model.generate_interpolation(samples_name, num_image_tiles, num_steps = interpolation_num_steps, save_frames = save_frames)
+        print(f'interpolation generated at {results_dir}/{name}/{samples_name}')
+        return
+
+    if show_progress:
+        model = Trainer(**model_args)
+        model.show_progress(num_images=num_image_tiles, types=generate_types)
+        return
+
+    run_training(model_args, data, load_from, new, num_train_steps, name, seed)
+
+def main():
+    fire.Fire(train_from_folder)
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/huggan/pytorch/lightweight_gan/diff_augment.py b/huggan/pytorch/lightweight_gan/diff_augment.py
new file mode 100644
index 0000000000000000000000000000000000000000..794e256a9bae5f63fbb799dda8200786990e6da9
--- /dev/null
+++ b/huggan/pytorch/lightweight_gan/diff_augment.py
@@ -0,0 +1,102 @@
+import random
+
+import torch
+import torch.nn.functional as F
+
+
+def DiffAugment(x, types=[]):
+    for p in types:
+        for f in AUGMENT_FNS[p]:
+            x = f(x)
+    return x.contiguous()
+
+
+# """
+# Augmentation functions got images as `x`
+# where `x` is tensor with this dimensions:
+# 0 - count of images
+# 1 - channels
+# 2 - width
+# 3 - height of image
+# """
+
+def rand_brightness(x):
+    x = x + (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) - 0.5)
+    return x
+
+def rand_saturation(x):
+    x_mean = x.mean(dim=1, keepdim=True)
+    x = (x - x_mean) * (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) * 2) + x_mean
+    return x
+
+def rand_contrast(x):
+    x_mean = x.mean(dim=[1, 2, 3], keepdim=True)
+    x = (x - x_mean) * (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) + 0.5) + x_mean
+    return x
+
+def rand_translation(x, ratio=0.125):
+    shift_x, shift_y = int(x.size(2) * ratio + 0.5), int(x.size(3) * ratio + 0.5)
+    translation_x = torch.randint(-shift_x, shift_x + 1, size=[x.size(0), 1, 1], device=x.device)
+    translation_y = torch.randint(-shift_y, shift_y + 1, size=[x.size(0), 1, 1], device=x.device)
+    grid_batch, grid_x, grid_y = torch.meshgrid(
+        torch.arange(x.size(0), dtype=torch.long, device=x.device),
+        torch.arange(x.size(2), dtype=torch.long, device=x.device),
+        torch.arange(x.size(3), dtype=torch.long, device=x.device),
+    indexing = 'ij')
+    grid_x = torch.clamp(grid_x + translation_x + 1, 0, x.size(2) + 1)
+    grid_y = torch.clamp(grid_y + translation_y + 1, 0, x.size(3) + 1)
+    x_pad = F.pad(x, [1, 1, 1, 1, 0, 0, 0, 0])
+    x = x_pad.permute(0, 2, 3, 1).contiguous()[grid_batch, grid_x, grid_y].permute(0, 3, 1, 2)
+    return x
+
+def rand_offset(x, ratio=1, ratio_h=1, ratio_v=1):
+    w, h = x.size(2), x.size(3)
+
+    imgs = []
+    for img in x.unbind(dim = 0):
+        max_h = int(w * ratio * ratio_h)
+        max_v = int(h * ratio * ratio_v)
+
+        value_h = random.randint(0, max_h) * 2 - max_h
+        value_v = random.randint(0, max_v) * 2 - max_v
+
+        if abs(value_h) > 0:
+            img = torch.roll(img, value_h, 2)
+
+        if abs(value_v) > 0:
+            img = torch.roll(img, value_v, 1)
+
+        imgs.append(img)
+
+    return torch.stack(imgs)
+
+def rand_offset_h(x, ratio=1):
+    return rand_offset(x, ratio=1, ratio_h=ratio, ratio_v=0)
+
+def rand_offset_v(x, ratio=1):
+    return rand_offset(x, ratio=1, ratio_h=0, ratio_v=ratio)
+
+def rand_cutout(x, ratio=0.5):
+    cutout_size = int(x.size(2) * ratio + 0.5), int(x.size(3) * ratio + 0.5)
+    offset_x = torch.randint(0, x.size(2) + (1 - cutout_size[0] % 2), size=[x.size(0), 1, 1], device=x.device)
+    offset_y = torch.randint(0, x.size(3) + (1 - cutout_size[1] % 2), size=[x.size(0), 1, 1], device=x.device)
+    grid_batch, grid_x, grid_y = torch.meshgrid(
+        torch.arange(x.size(0), dtype=torch.long, device=x.device),
+        torch.arange(cutout_size[0], dtype=torch.long, device=x.device),
+        torch.arange(cutout_size[1], dtype=torch.long, device=x.device),
+    indexing = 'ij')
+    grid_x = torch.clamp(grid_x + offset_x - cutout_size[0] // 2, min=0, max=x.size(2) - 1)
+    grid_y = torch.clamp(grid_y + offset_y - cutout_size[1] // 2, min=0, max=x.size(3) - 1)
+    mask = torch.ones(x.size(0), x.size(2), x.size(3), dtype=x.dtype, device=x.device)
+    mask[grid_batch, grid_x, grid_y] = 0
+    x = x * mask.unsqueeze(1)
+    return x
+
+AUGMENT_FNS = {
+    'color': [rand_brightness, rand_saturation, rand_contrast],
+    'offset': [rand_offset],
+    'offset_h': [rand_offset_h],
+    'offset_v': [rand_offset_v],
+    'translation': [rand_translation],
+    'cutout': [rand_cutout],
+}
\ No newline at end of file
diff --git a/huggan/pytorch/lightweight_gan/lightweight_gan.py b/huggan/pytorch/lightweight_gan/lightweight_gan.py
new file mode 100644
index 0000000000000000000000000000000000000000..bbacd4ac5c49e544ebcfa299ad5637bc369916d2
--- /dev/null
+++ b/huggan/pytorch/lightweight_gan/lightweight_gan.py
@@ -0,0 +1,1598 @@
+import os
+import json
+import tempfile
+from random import random
+import math
+from math import log2, floor
+from pathlib import Path
+from functools import partial
+from contextlib import contextmanager, ExitStack
+from pathlib import Path
+from shutil import rmtree
+
+import torch
+from torch.optim import Adam
+from torch import nn, einsum
+import torch.nn.functional as F
+from torch.utils.data import Dataset, DataLoader
+from torch.autograd import grad as torch_grad
+
+from PIL import Image
+import torchvision
+from torchvision import transforms
+from torchvision.utils import save_image
+from kornia.filters import filter2d
+
+from huggan.pytorch.lightweight_gan.diff_augment import DiffAugment
+
+from tqdm import tqdm
+from einops import rearrange, reduce, repeat
+
+from datasets import load_dataset
+
+from accelerate import Accelerator, DistributedDataParallelKwargs
+from huggingface_hub import hf_hub_download, create_repo
+
+from huggan.pytorch.huggan_mixin import HugGANModelHubMixin
+from huggan.utils.hub import get_full_repo_name
+
+# constants
+
+# NUM_CORES = multiprocessing.cpu_count()
+EXTS = ['jpg', 'jpeg', 'png']
+PYTORCH_WEIGHTS_NAME = 'model.pt'
+
+
+# helpers
+
+def exists(val):
+    return val is not None
+
+
+@contextmanager
+def null_context():
+    yield
+
+
+def is_power_of_two(val):
+    return log2(val).is_integer()
+
+
+def default(val, d):
+    return val if exists(val) else d
+
+
+def set_requires_grad(model, bool):
+    for p in model.parameters():
+        p.requires_grad = bool
+
+
+def cycle(iterable):
+    while True:
+        for i in iterable:
+            yield i
+
+
+def raise_if_nan(t):
+    if torch.isnan(t):
+        raise NanException
+
+
+def evaluate_in_chunks(max_batch_size, model, *args):
+    split_args = list(zip(*list(map(lambda x: x.split(max_batch_size, dim=0), args))))
+    chunked_outputs = [model(*i) for i in split_args]
+    if len(chunked_outputs) == 1:
+        return chunked_outputs[0]
+    return torch.cat(chunked_outputs, dim=0)
+
+
+def slerp(val, low, high):
+    low_norm = low / torch.norm(low, dim=1, keepdim=True)
+    high_norm = high / torch.norm(high, dim=1, keepdim=True)
+    omega = torch.acos((low_norm * high_norm).sum(1))
+    so = torch.sin(omega)
+    res = (torch.sin((1.0 - val) * omega) / so).unsqueeze(1) * low + (torch.sin(val * omega) / so).unsqueeze(1) * high
+    return res
+
+
+def safe_div(n, d):
+    try:
+        res = n / d
+    except ZeroDivisionError:
+        prefix = '' if int(n >= 0) else '-'
+        res = float(f'{prefix}inf')
+    return res
+
+
+# loss functions
+
+def gen_hinge_loss(fake, real):
+    return fake.mean()
+
+
+def hinge_loss(real, fake):
+    return (F.relu(1 + real) + F.relu(1 - fake)).mean()
+
+
+def dual_contrastive_loss(real_logits, fake_logits):
+    device = real_logits.device
+    real_logits, fake_logits = map(lambda t: rearrange(t, '... -> (...)'), (real_logits, fake_logits))
+
+    def loss_half(t1, t2):
+        t1 = rearrange(t1, 'i -> i ()')
+        t2 = repeat(t2, 'j -> i j', i=t1.shape[0])
+        t = torch.cat((t1, t2), dim=-1)
+        return F.cross_entropy(t, torch.zeros(t1.shape[0], device=device, dtype=torch.long))
+
+    return loss_half(real_logits, fake_logits) + loss_half(-fake_logits, -real_logits)
+
+
+# helper classes
+
+class NanException(Exception):
+    pass
+
+
+class EMA():
+    def __init__(self, beta):
+        super().__init__()
+        self.beta = beta
+
+    def update_average(self, old, new):
+        if not exists(old):
+            return new
+        return old * self.beta + (1 - self.beta) * new
+
+
+class RandomApply(nn.Module):
+    def __init__(self, prob, fn, fn_else=lambda x: x):
+        super().__init__()
+        self.fn = fn
+        self.fn_else = fn_else
+        self.prob = prob
+
+    def forward(self, x):
+        fn = self.fn if random() < self.prob else self.fn_else
+        return fn(x)
+
+
+class ChanNorm(nn.Module):
+    def __init__(self, dim, eps=1e-5):
+        super().__init__()
+        self.eps = eps
+        self.g = nn.Parameter(torch.ones(1, dim, 1, 1))
+        self.b = nn.Parameter(torch.zeros(1, dim, 1, 1))
+
+    def forward(self, x):
+        var = torch.var(x, dim=1, unbiased=False, keepdim=True)
+        mean = torch.mean(x, dim=1, keepdim=True)
+        return (x - mean) / (var + self.eps).sqrt() * self.g + self.b
+
+
+class PreNorm(nn.Module):
+    def __init__(self, dim, fn):
+        super().__init__()
+        self.fn = fn
+        self.norm = ChanNorm(dim)
+
+    def forward(self, x):
+        return self.fn(self.norm(x))
+
+
+class Residual(nn.Module):
+    def __init__(self, fn):
+        super().__init__()
+        self.fn = fn
+
+    def forward(self, x):
+        return self.fn(x) + x
+
+
+class SumBranches(nn.Module):
+    def __init__(self, branches):
+        super().__init__()
+        self.branches = nn.ModuleList(branches)
+
+    def forward(self, x):
+        return sum(map(lambda fn: fn(x), self.branches))
+
+
+class Fuzziness(nn.Module):
+    def __init__(self):
+        super().__init__()
+        f = torch.Tensor([1, 2, 1])
+        self.register_buffer('f', f)
+
+    def forward(self, x):
+        f = self.f
+        f = f[None, None, :] * f[None, :, None]
+        return filter2d(x, f, normalized=True)
+
+
+Blur = nn.Identity
+
+
+# attention
+
+class DepthWiseConv2d(nn.Module):
+    def __init__(self, dim_in, dim_out, kernel_size, padding=0, stride=1, bias=True):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Conv2d(dim_in, dim_in, kernel_size=kernel_size, padding=padding, groups=dim_in, stride=stride,
+                      bias=bias),
+            nn.Conv2d(dim_in, dim_out, kernel_size=1, bias=bias)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+
+class LinearAttention(nn.Module):
+    def __init__(self, dim, dim_head=64, heads=8):
+        super().__init__()
+        self.scale = dim_head ** -0.5
+        self.heads = heads
+        inner_dim = dim_head * heads
+
+        self.nonlin = nn.GELU()
+        self.to_q = nn.Conv2d(dim, inner_dim, 1, bias=False)
+        self.to_kv = DepthWiseConv2d(dim, inner_dim * 2, 3, padding=1, bias=False)
+        self.to_out = nn.Conv2d(inner_dim, dim, 1)
+
+    def forward(self, fmap):
+        h, x, y = self.heads, *fmap.shape[-2:]
+        q, k, v = (self.to_q(fmap), *self.to_kv(fmap).chunk(2, dim=1))
+        q, k, v = map(lambda t: rearrange(t, 'b (h c) x y -> (b h) (x y) c', h=h), (q, k, v))
+
+        q = q.softmax(dim=-1)
+        k = k.softmax(dim=-2)
+
+        q = q * self.scale
+
+        context = einsum('b n d, b n e -> b d e', k, v)
+        out = einsum('b n d, b d e -> b n e', q, context)
+        out = rearrange(out, '(b h) (x y) d -> b (h d) x y', h=h, x=x, y=y)
+
+        out = self.nonlin(out)
+        return self.to_out(out)
+
+
+# dataset
+
+def convert_image_to(img_type, image):
+    if image.mode != img_type:
+        return image.convert(img_type)
+    return image
+
+
+class identity(object):
+    def __call__(self, tensor):
+        return tensor
+
+
+class expand_greyscale(object):
+    def __init__(self, transparent):
+        self.transparent = transparent
+
+    def __call__(self, tensor):
+        channels = tensor.shape[0]
+        num_target_channels = 4 if self.transparent else 3
+
+        if channels == num_target_channels:
+            return tensor
+
+        alpha = None
+        if channels == 1:
+            color = tensor.expand(3, -1, -1)
+        elif channels == 2:
+            color = tensor[:1].expand(3, -1, -1)
+            alpha = tensor[1:]
+        else:
+            raise Exception(f'image with invalid number of channels given {channels}')
+
+        if not exists(alpha) and self.transparent:
+            alpha = torch.ones(1, *tensor.shape[1:], device=tensor.device)
+
+        return color if not self.transparent else torch.cat((color, alpha))
+
+
+def resize_to_minimum_size(min_size, image):
+    if max(*image.size) < min_size:
+        return torchvision.transforms.functional.resize(image, min_size)
+    return image
+
+
+# augmentations
+
+def random_hflip(tensor, prob):
+    if prob > random():
+        return tensor
+    return torch.flip(tensor, dims=(3,))
+
+
+class AugWrapper(nn.Module):
+    def __init__(self, D, image_size):
+        super().__init__()
+        self.D = D
+
+    def forward(self, images, prob=0., types=[], detach=False, **kwargs):
+        context = torch.no_grad if detach else null_context
+
+        with context():
+            if random() < prob:
+                images = random_hflip(images, prob=0.5)
+                images = DiffAugment(images, types=types)
+
+        return self.D(images, **kwargs)
+
+
+# modifiable global variables
+
+norm_class = nn.BatchNorm2d
+
+
+def upsample(scale_factor=2):
+    return nn.Upsample(scale_factor=scale_factor)
+
+
+# squeeze excitation classes
+
+# global context network
+# https://arxiv.org/abs/2012.13375
+# similar to squeeze-excite, but with a simplified attention pooling and a subsequent layer norm
+
+class GlobalContext(nn.Module):
+    def __init__(
+            self,
+            *,
+            chan_in,
+            chan_out
+    ):
+        super().__init__()
+        self.to_k = nn.Conv2d(chan_in, 1, 1)
+        chan_intermediate = max(3, chan_out // 2)
+
+        self.net = nn.Sequential(
+            nn.Conv2d(chan_in, chan_intermediate, 1),
+            nn.LeakyReLU(0.1),
+            nn.Conv2d(chan_intermediate, chan_out, 1),
+            nn.Sigmoid()
+        )
+
+    def forward(self, x):
+        context = self.to_k(x)
+        context = context.flatten(2).softmax(dim=-1)
+        out = einsum('b i n, b c n -> b c i', context, x.flatten(2))
+        out = out.unsqueeze(-1)
+        return self.net(out)
+
+
+# frequency channel attention
+# https://arxiv.org/abs/2012.11879
+
+def get_1d_dct(i, freq, L):
+    result = math.cos(math.pi * freq * (i + 0.5) / L) / math.sqrt(L)
+    return result * (1 if freq == 0 else math.sqrt(2))
+
+
+def get_dct_weights(width, channel, fidx_u, fidx_v):
+    dct_weights = torch.zeros(1, channel, width, width)
+    c_part = channel // len(fidx_u)
+
+    for i, (u_x, v_y) in enumerate(zip(fidx_u, fidx_v)):
+        for x in range(width):
+            for y in range(width):
+                coor_value = get_1d_dct(x, u_x, width) * get_1d_dct(y, v_y, width)
+                dct_weights[:, i * c_part: (i + 1) * c_part, x, y] = coor_value
+
+    return dct_weights
+
+
+class FCANet(nn.Module):
+    def __init__(
+            self,
+            *,
+            chan_in,
+            chan_out,
+            reduction=4,
+            width
+    ):
+        super().__init__()
+
+        freq_w, freq_h = ([0] * 8), list(range(8))  # in paper, it seems 16 frequencies was ideal
+        dct_weights = get_dct_weights(width, chan_in, [*freq_w, *freq_h], [*freq_h, *freq_w])
+        self.register_buffer('dct_weights', dct_weights)
+
+        chan_intermediate = max(3, chan_out // reduction)
+
+        self.net = nn.Sequential(
+            nn.Conv2d(chan_in, chan_intermediate, 1),
+            nn.LeakyReLU(0.1),
+            nn.Conv2d(chan_intermediate, chan_out, 1),
+            nn.Sigmoid()
+        )
+
+    def forward(self, x):
+        x = reduce(x * self.dct_weights, 'b c (h h1) (w w1) -> b c h1 w1', 'sum', h1=1, w1=1)
+        return self.net(x)
+
+
+# generative adversarial network
+
+class Generator(nn.Module):
+    def __init__(
+            self,
+            *,
+            image_size,
+            latent_dim=256,
+            fmap_max=512,
+            fmap_inverse_coef=12,
+            transparent=False,
+            greyscale=False,
+            attn_res_layers=[],
+            freq_chan_attn=False
+    ):
+        super().__init__()
+        resolution = log2(image_size)
+        assert is_power_of_two(image_size), 'image size must be a power of 2'
+
+        if transparent:
+            init_channel = 4
+        elif greyscale:
+            init_channel = 1
+        else:
+            init_channel = 3
+
+        fmap_max = default(fmap_max, latent_dim)
+
+        self.initial_conv = nn.Sequential(
+            nn.ConvTranspose2d(latent_dim, latent_dim * 2, 4),
+            norm_class(latent_dim * 2),
+            nn.GLU(dim=1)
+        )
+
+        num_layers = int(resolution) - 2
+        features = list(map(lambda n: (n, 2 ** (fmap_inverse_coef - n)), range(2, num_layers + 2)))
+        features = list(map(lambda n: (n[0], min(n[1], fmap_max)), features))
+        features = list(map(lambda n: 3 if n[0] >= 8 else n[1], features))
+        features = [latent_dim, *features]
+
+        in_out_features = list(zip(features[:-1], features[1:]))
+
+        self.res_layers = range(2, num_layers + 2)
+        self.layers = nn.ModuleList([])
+        self.res_to_feature_map = dict(zip(self.res_layers, in_out_features))
+
+        self.sle_map = ((3, 7), (4, 8), (5, 9), (6, 10))
+        self.sle_map = list(filter(lambda t: t[0] <= resolution and t[1] <= resolution, self.sle_map))
+        self.sle_map = dict(self.sle_map)
+
+        self.num_layers_spatial_res = 1
+
+        for (res, (chan_in, chan_out)) in zip(self.res_layers, in_out_features):
+            image_width = 2 ** res
+
+            attn = None
+            if image_width in attn_res_layers:
+                attn = PreNorm(chan_in, LinearAttention(chan_in))
+
+            sle = None
+            if res in self.sle_map:
+                residual_layer = self.sle_map[res]
+                sle_chan_out = self.res_to_feature_map[residual_layer - 1][-1]
+
+                if freq_chan_attn:
+                    sle = FCANet(
+                        chan_in=chan_out,
+                        chan_out=sle_chan_out,
+                        width=2 ** (res + 1)
+                    )
+                else:
+                    sle = GlobalContext(
+                        chan_in=chan_out,
+                        chan_out=sle_chan_out
+                    )
+
+            layer = nn.ModuleList([
+                nn.Sequential(
+                    upsample(),
+                    Blur(),
+                    nn.Conv2d(chan_in, chan_out * 2, 3, padding=1),
+                    norm_class(chan_out * 2),
+                    nn.GLU(dim=1)
+                ),
+                sle,
+                attn
+            ])
+            self.layers.append(layer)
+
+        self.out_conv = nn.Conv2d(features[-1], init_channel, 3, padding=1)
+
+    def forward(self, x):
+        x = rearrange(x, 'b c -> b c () ()')
+        x = self.initial_conv(x)
+        x = F.normalize(x, dim=1)
+
+        residuals = dict()
+
+        for (res, (up, sle, attn)) in zip(self.res_layers, self.layers):
+            if exists(attn):
+                x = attn(x) + x
+
+            x = up(x)
+
+            if exists(sle):
+                out_res = self.sle_map[res]
+                residual = sle(x)
+                residuals[out_res] = residual
+
+            next_res = res + 1
+            if next_res in residuals:
+                x = x * residuals[next_res]
+
+        return self.out_conv(x)
+
+
+class SimpleDecoder(nn.Module):
+    def __init__(
+            self,
+            *,
+            chan_in,
+            chan_out=3,
+            num_upsamples=4,
+    ):
+        super().__init__()
+
+        self.layers = nn.ModuleList([])
+        final_chan = chan_out
+        chans = chan_in
+
+        for ind in range(num_upsamples):
+            last_layer = ind == (num_upsamples - 1)
+            chan_out = chans if not last_layer else final_chan * 2
+            layer = nn.Sequential(
+                upsample(),
+                nn.Conv2d(chans, chan_out, 3, padding=1),
+                nn.GLU(dim=1)
+            )
+            self.layers.append(layer)
+            chans //= 2
+
+    def forward(self, x):
+        for layer in self.layers:
+            x = layer(x)
+        return x
+
+
+class Discriminator(nn.Module):
+    def __init__(
+            self,
+            *,
+            image_size,
+            fmap_max=512,
+            fmap_inverse_coef=12,
+            transparent=False,
+            greyscale=False,
+            disc_output_size=5,
+            attn_res_layers=[]
+    ):
+        super().__init__()
+        resolution = log2(image_size)
+        assert is_power_of_two(image_size), 'image size must be a power of 2'
+        assert disc_output_size in {1, 5}, 'discriminator output dimensions can only be 5x5 or 1x1'
+
+        resolution = int(resolution)
+
+        if transparent:
+            init_channel = 4
+        elif greyscale:
+            init_channel = 1
+        else:
+            init_channel = 3
+
+        num_non_residual_layers = max(0, int(resolution) - 8)
+        num_residual_layers = 8 - 3
+
+        non_residual_resolutions = range(min(8, resolution), 2, -1)
+        features = list(map(lambda n: (n, 2 ** (fmap_inverse_coef - n)), non_residual_resolutions))
+        features = list(map(lambda n: (n[0], min(n[1], fmap_max)), features))
+
+        if num_non_residual_layers == 0:
+            res, _ = features[0]
+            features[0] = (res, init_channel)
+
+        chan_in_out = list(zip(features[:-1], features[1:]))
+
+        self.non_residual_layers = nn.ModuleList([])
+        for ind in range(num_non_residual_layers):
+            first_layer = ind == 0
+            last_layer = ind == (num_non_residual_layers - 1)
+            chan_out = features[0][-1] if last_layer else init_channel
+
+            self.non_residual_layers.append(nn.Sequential(
+                Blur(),
+                nn.Conv2d(init_channel, chan_out, 4, stride=2, padding=1),
+                nn.LeakyReLU(0.1)
+            ))
+
+        self.residual_layers = nn.ModuleList([])
+
+        for (res, ((_, chan_in), (_, chan_out))) in zip(non_residual_resolutions, chan_in_out):
+            image_width = 2 ** res
+
+            attn = None
+            if image_width in attn_res_layers:
+                attn = PreNorm(chan_in, LinearAttention(chan_in))
+
+            self.residual_layers.append(nn.ModuleList([
+                SumBranches([
+                    nn.Sequential(
+                        Blur(),
+                        nn.Conv2d(chan_in, chan_out, 4, stride=2, padding=1),
+                        nn.LeakyReLU(0.1),
+                        nn.Conv2d(chan_out, chan_out, 3, padding=1),
+                        nn.LeakyReLU(0.1)
+                    ),
+                    nn.Sequential(
+                        Blur(),
+                        nn.AvgPool2d(2),
+                        nn.Conv2d(chan_in, chan_out, 1),
+                        nn.LeakyReLU(0.1),
+                    )
+                ]),
+                attn
+            ]))
+
+        last_chan = features[-1][-1]
+        if disc_output_size == 5:
+            self.to_logits = nn.Sequential(
+                nn.Conv2d(last_chan, last_chan, 1),
+                nn.LeakyReLU(0.1),
+                nn.Conv2d(last_chan, 1, 4)
+            )
+        elif disc_output_size == 1:
+            self.to_logits = nn.Sequential(
+                Blur(),
+                nn.Conv2d(last_chan, last_chan, 3, stride=2, padding=1),
+                nn.LeakyReLU(0.1),
+                nn.Conv2d(last_chan, 1, 4)
+            )
+
+        self.to_shape_disc_out = nn.Sequential(
+            nn.Conv2d(init_channel, 64, 3, padding=1),
+            Residual(PreNorm(64, LinearAttention(64))),
+            SumBranches([
+                nn.Sequential(
+                    Blur(),
+                    nn.Conv2d(64, 32, 4, stride=2, padding=1),
+                    nn.LeakyReLU(0.1),
+                    nn.Conv2d(32, 32, 3, padding=1),
+                    nn.LeakyReLU(0.1)
+                ),
+                nn.Sequential(
+                    Blur(),
+                    nn.AvgPool2d(2),
+                    nn.Conv2d(64, 32, 1),
+                    nn.LeakyReLU(0.1),
+                )
+            ]),
+            Residual(PreNorm(32, LinearAttention(32))),
+            nn.AdaptiveAvgPool2d((4, 4)),
+            nn.Conv2d(32, 1, 4)
+        )
+
+        self.decoder1 = SimpleDecoder(chan_in=last_chan, chan_out=init_channel)
+        self.decoder2 = SimpleDecoder(chan_in=features[-2][-1], chan_out=init_channel) if resolution >= 9 else None
+
+    def forward(self, x, calc_aux_loss=False):
+        orig_img = x
+
+        for layer in self.non_residual_layers:
+            x = layer(x)
+
+        layer_outputs = []
+
+        for (net, attn) in self.residual_layers:
+            if exists(attn):
+                x = attn(x) + x
+
+            x = net(x)
+            layer_outputs.append(x)
+
+        out = self.to_logits(x).flatten(1)
+
+        img_32x32 = F.interpolate(orig_img, size=(32, 32))
+        out_32x32 = self.to_shape_disc_out(img_32x32)
+
+        if not calc_aux_loss:
+            return out, out_32x32, None
+
+        # self-supervised auto-encoding loss
+
+        layer_8x8 = layer_outputs[-1]
+        layer_16x16 = layer_outputs[-2]
+
+        recon_img_8x8 = self.decoder1(layer_8x8)
+
+        aux_loss = F.mse_loss(
+            recon_img_8x8,
+            F.interpolate(orig_img, size=recon_img_8x8.shape[2:])
+        )
+
+        if exists(self.decoder2):
+            select_random_quadrant = lambda rand_quadrant, img: \
+                rearrange(img, 'b c (m h) (n w) -> (m n) b c h w', m=2, n=2)[rand_quadrant]
+            crop_image_fn = partial(select_random_quadrant, floor(random() * 4))
+            img_part, layer_16x16_part = map(crop_image_fn, (orig_img, layer_16x16))
+
+            recon_img_16x16 = self.decoder2(layer_16x16_part)
+
+            aux_loss_16x16 = F.mse_loss(
+                recon_img_16x16,
+                F.interpolate(img_part, size=recon_img_16x16.shape[2:])
+            )
+
+            aux_loss = aux_loss + aux_loss_16x16
+
+        return out, out_32x32, aux_loss
+
+
+class LightweightGAN(nn.Module, HugGANModelHubMixin):
+    def __init__(
+            self,
+            *,
+            latent_dim,
+            image_size,
+            optimizer="adam",
+            fmap_max=512,
+            fmap_inverse_coef=12,
+            transparent=False,
+            greyscale=False,
+            disc_output_size=5,
+            attn_res_layers=[],
+            freq_chan_attn=False,
+            ttur_mult=1.,
+            lr=2e-4,
+    ):
+        super().__init__()
+
+        self.config = {
+            'latent_dim': latent_dim,
+            'image_size': image_size,
+            'optimizer': optimizer,
+            'fmap_max': fmap_max,
+            'fmap_inverse_coef': fmap_inverse_coef,
+            'transparent': transparent,
+            'greyscale': greyscale,
+            'disc_output_size': disc_output_size,
+            'attn_res_layers': attn_res_layers,
+            'freq_chan_attn': freq_chan_attn,
+            'ttur_mult': ttur_mult,
+            'lr': lr
+        }
+
+        self.latent_dim = latent_dim
+        self.image_size = image_size
+
+        G_kwargs = dict(
+            image_size=image_size,
+            latent_dim=latent_dim,
+            fmap_max=fmap_max,
+            fmap_inverse_coef=fmap_inverse_coef,
+            transparent=transparent,
+            greyscale=greyscale,
+            attn_res_layers=attn_res_layers,
+            freq_chan_attn=freq_chan_attn
+        )
+
+        self.G = Generator(**G_kwargs)
+
+        self.D = Discriminator(
+            image_size=image_size,
+            fmap_max=fmap_max,
+            fmap_inverse_coef=fmap_inverse_coef,
+            transparent=transparent,
+            greyscale=greyscale,
+            attn_res_layers=attn_res_layers,
+            disc_output_size=disc_output_size
+        )
+
+        self.ema_updater = EMA(0.995)
+        self.GE = Generator(**G_kwargs)
+        set_requires_grad(self.GE, False)
+
+        if optimizer == "adam":
+            self.G_opt = Adam(self.G.parameters(), lr=lr, betas=(0.5, 0.9))
+            self.D_opt = Adam(self.D.parameters(), lr=lr * ttur_mult, betas=(0.5, 0.9))
+        elif optimizer == "adabelief":
+            from adabelief_pytorch import AdaBelief
+
+            self.G_opt = AdaBelief(self.G.parameters(), lr=lr, betas=(0.5, 0.9))
+            self.D_opt = AdaBelief(self.D.parameters(), lr=lr * ttur_mult, betas=(0.5, 0.9))
+        else:
+            assert False, "No valid optimizer is given"
+
+        self.apply(self._init_weights)
+        self.reset_parameter_averaging()
+
+        self.D_aug = AugWrapper(self.D, image_size)
+
+    def _init_weights(self, m):
+        if type(m) in {nn.Conv2d, nn.Linear}:
+            nn.init.kaiming_normal_(m.weight, a=0, mode='fan_in', nonlinearity='leaky_relu')
+
+    def EMA(self):
+        def update_moving_average(ma_model, current_model):
+            for current_params, ma_params in zip(current_model.parameters(), ma_model.parameters()):
+                old_weight, up_weight = ma_params.data, current_params.data
+                ma_params.data = self.ema_updater.update_average(old_weight, up_weight)
+
+            for current_buffer, ma_buffer in zip(current_model.buffers(), ma_model.buffers()):
+                new_buffer_value = self.ema_updater.update_average(ma_buffer, current_buffer)
+                ma_buffer.copy_(new_buffer_value)
+
+        update_moving_average(self.GE, self.G)
+
+    def reset_parameter_averaging(self):
+        self.GE.load_state_dict(self.G.state_dict())
+
+    def forward(self, x):
+        raise NotImplemented
+
+    def _save_pretrained(self, save_directory):
+        """
+        Overwrite this method in case you don't want to save complete model,
+        rather some specific layers
+        """
+        path = os.path.join(save_directory, PYTORCH_WEIGHTS_NAME)
+        model_to_save = self.module if hasattr(self, "module") else self
+
+        # We update this to be a dict containing 'GAN', as that's what is expected
+        torch.save({'GAN': model_to_save.state_dict()}, path)
+
+    @classmethod
+    def _from_pretrained(
+            cls,
+            model_id,
+            revision,
+            cache_dir,
+            force_download,
+            proxies,
+            resume_download,
+            local_files_only,
+            token,
+            map_location="cpu",
+            strict=False,
+            **model_kwargs,
+    ):
+        """
+        Overwrite this method in case you wish to initialize your model in a
+        different way.
+        """
+        map_location = torch.device(map_location)
+
+        if os.path.isdir(model_id):
+            print("Loading weights from local directory")
+            model_file = os.path.join(model_id, PYTORCH_WEIGHTS_NAME)
+        else:
+            model_file = hf_hub_download(
+                repo_id=model_id,
+                filename=PYTORCH_WEIGHTS_NAME,
+                revision=revision,
+                cache_dir=cache_dir,
+                force_download=force_download,
+                proxies=proxies,
+                resume_download=resume_download,
+                token=token,
+                local_files_only=local_files_only,
+            )
+
+        # We update here to directly unpack config
+        model = cls(**model_kwargs['config'])
+
+        state_dict = torch.load(model_file, map_location=map_location)
+        model.load_state_dict(state_dict["GAN"], strict=strict)
+        model.eval()
+
+        return model
+
+
+# trainer
+
+class Trainer():
+    def __init__(
+            self,
+            dataset_name="huggan/CelebA-faces",
+            name='default',
+            results_dir='results',
+            models_dir='models',
+            base_dir='./',
+            optimizer='adam',
+            latent_dim=256,
+            image_size=128,
+            num_image_tiles=8,
+            fmap_max=512,
+            transparent=False,
+            greyscale=False,
+            batch_size=4,
+            gp_weight=10,
+            gradient_accumulate_every=1,
+            attn_res_layers=[],
+            freq_chan_attn=False,
+            disc_output_size=5,
+            dual_contrast_loss=False,
+            antialias=False,
+            lr=2e-4,
+            lr_mlp=1.,
+            ttur_mult=1.,
+            save_every=10000,
+            evaluate_every=1000,
+            aug_prob=None,
+            aug_types=['translation', 'cutout'],
+            dataset_aug_prob=0.,
+            calculate_fid_every=None,
+            calculate_fid_num_images=12800,
+            clear_fid_cache=False,
+            log=False,
+            cpu=False,
+            mixed_precision="no",
+            wandb=False,
+            push_to_hub=False,
+            organization_name=None,
+            *args,
+            **kwargs
+    ):
+        self.GAN_params = [args, kwargs]
+        self.GAN = None
+
+        self.dataset_name = dataset_name
+
+        self.name = name
+
+        base_dir = Path(base_dir)
+        self.base_dir = base_dir
+        self.results_dir = base_dir / results_dir
+        self.models_dir = base_dir / models_dir
+        self.fid_dir = base_dir / 'fid' / name
+
+        # Note - in original repo config is private - ".config.json", but here, we make it public
+        self.config_path = self.models_dir / name / 'config.json'
+
+        assert is_power_of_two(image_size), 'image size must be a power of 2 (64, 128, 256, 512, 1024)'
+        assert all(map(is_power_of_two,
+                       attn_res_layers)), 'resolution layers of attention must all be powers of 2 (16, 32, 64, 128, 256, 512)'
+
+        assert not (
+                dual_contrast_loss and disc_output_size > 1), 'discriminator output size cannot be greater than 1 if using dual contrastive loss'
+
+        self.image_size = image_size
+        self.num_image_tiles = num_image_tiles
+
+        self.latent_dim = latent_dim
+        self.fmap_max = fmap_max
+        self.transparent = transparent
+        self.greyscale = greyscale
+
+        assert (int(self.transparent) + int(self.greyscale)) < 2, 'you can only set either transparency or greyscale'
+
+        self.aug_prob = aug_prob
+        self.aug_types = aug_types
+
+        self.lr = lr
+        self.optimizer = optimizer
+        self.ttur_mult = ttur_mult
+        self.batch_size = batch_size
+        self.gradient_accumulate_every = gradient_accumulate_every
+
+        self.gp_weight = gp_weight
+
+        self.evaluate_every = evaluate_every
+        self.save_every = save_every
+        self.steps = 0
+
+        self.attn_res_layers = attn_res_layers
+        self.freq_chan_attn = freq_chan_attn
+
+        self.disc_output_size = disc_output_size
+        self.antialias = antialias
+
+        self.dual_contrast_loss = dual_contrast_loss
+
+        self.d_loss = 0
+        self.g_loss = 0
+        self.last_gp_loss = None
+        self.last_recon_loss = None
+        self.last_fid = None
+
+        self.init_folders()
+
+        self.loader = None
+        self.dataset_aug_prob = dataset_aug_prob
+
+        self.calculate_fid_every = calculate_fid_every
+        self.calculate_fid_num_images = calculate_fid_num_images
+        self.clear_fid_cache = clear_fid_cache
+
+        self.syncbatchnorm = torch.cuda.device_count() > 1 and not cpu
+
+        self.cpu = cpu
+        self.mixed_precision = mixed_precision
+
+        self.wandb = wandb
+
+        self.push_to_hub = push_to_hub
+        self.organization_name = organization_name
+        self.repo_name = get_full_repo_name(self.name, self.organization_name)
+        if self.push_to_hub:
+            self.repo_url = create_repo(self.repo_name, exist_ok=True)
+
+    @property
+    def image_extension(self):
+        return 'jpg' if not self.transparent else 'png'
+
+    @property
+    def checkpoint_num(self):
+        return floor(self.steps // self.save_every)
+
+    def init_GAN(self):
+        args, kwargs = self.GAN_params
+
+        # set some global variables before instantiating GAN
+
+        global norm_class
+        global Blur
+
+        norm_class = nn.SyncBatchNorm if self.syncbatchnorm else nn.BatchNorm2d
+        Blur = nn.Identity if not self.antialias else Fuzziness
+
+        # instantiate GAN
+
+        self.GAN = LightweightGAN(
+            optimizer=self.optimizer,
+            lr=self.lr,
+            latent_dim=self.latent_dim,
+            attn_res_layers=self.attn_res_layers,
+            freq_chan_attn=self.freq_chan_attn,
+            image_size=self.image_size,
+            ttur_mult=self.ttur_mult,
+            fmap_max=self.fmap_max,
+            disc_output_size=self.disc_output_size,
+            transparent=self.transparent,
+            greyscale=self.greyscale,
+            *args,
+            **kwargs
+        )
+
+    def write_config(self):
+        self.config_path.write_text(json.dumps(self.config()))
+
+    def load_config(self):
+        config = self.config() if not self.config_path.exists() else json.loads(self.config_path.read_text())
+        self.image_size = config['image_size']
+        self.transparent = config['transparent']
+        self.syncbatchnorm = config['syncbatchnorm']
+        self.disc_output_size = config['disc_output_size']
+        self.greyscale = config.pop('greyscale', False)
+        self.attn_res_layers = config.pop('attn_res_layers', [])
+        self.freq_chan_attn = config.pop('freq_chan_attn', False)
+        self.optimizer = config.pop('optimizer', 'adam')
+        self.fmap_max = config.pop('fmap_max', 512)
+        del self.GAN
+        self.init_GAN()
+
+    def config(self):
+        return {
+            'image_size': self.image_size,
+            'transparent': self.transparent,
+            'greyscale': self.greyscale,
+            'syncbatchnorm': self.syncbatchnorm,
+            'disc_output_size': self.disc_output_size,
+            'optimizer': self.optimizer,
+            'attn_res_layers': self.attn_res_layers,
+            'freq_chan_attn': self.freq_chan_attn
+        }
+
+    def set_data_src(self):
+        # start of using HuggingFace dataset
+        dataset = load_dataset(self.dataset_name)
+
+        if self.transparent:
+            num_channels = 4
+            pillow_mode = 'RGBA'
+            expand_fn = expand_greyscale(self.transparent)
+        elif self.greyscale:
+            num_channels = 1
+            pillow_mode = 'L'
+            expand_fn = identity()
+        else:
+            num_channels = 3
+            pillow_mode = 'RGB'
+            expand_fn = expand_greyscale(self.transparent)
+
+        convert_image_fn = partial(convert_image_to, pillow_mode)
+
+        transform = transforms.Compose([
+            transforms.Lambda(convert_image_fn),
+            transforms.Lambda(partial(resize_to_minimum_size, self.image_size)),
+            transforms.Resize(self.image_size),
+            RandomApply(0., transforms.RandomResizedCrop(self.image_size, scale=(0.5, 1.0), ratio=(0.98, 1.02)),
+                        transforms.CenterCrop(self.image_size)),
+            transforms.ToTensor(),
+            transforms.Lambda(expand_fn)
+        ])
+
+        def transform_images(examples):
+            transformed_images = [transform(image.convert("RGB")) for image in examples["image"]]
+
+            examples["image"] = torch.stack(transformed_images)
+
+            return examples
+
+        transformed_dataset = dataset.with_transform(transform_images)
+
+        per_device_batch_size = math.ceil(self.batch_size / self.accelerator.num_processes)
+        dataloader = DataLoader(transformed_dataset["train"], per_device_batch_size, sampler=None, shuffle=False,
+                                drop_last=True, pin_memory=True)
+        num_samples = len(transformed_dataset)
+        ## end of HuggingFace dataset
+
+        # Note - in original repo, this is wrapped with cycle, but we will do that after accelerator prepares
+        self.loader = dataloader
+
+        # auto set augmentation prob for user if dataset is detected to be low
+        # num_samples = len(self.dataset)
+        if not exists(self.aug_prob) and num_samples < 1e5:
+            self.aug_prob = min(0.5, (1e5 - num_samples) * 3e-6)
+            print(f'autosetting augmentation probability to {round(self.aug_prob * 100)}%')
+
+    def init_accelerator(self):
+        # Initialize the accelerator. We will let the accelerator handle device placement.
+        ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
+        self.accelerator = Accelerator(kwargs_handlers=[ddp_kwargs], mixed_precision=self.mixed_precision, cpu=self.cpu)
+        
+        if self.accelerator.is_local_main_process:
+            # set up Weights and Biases if requested
+            if self.wandb:
+                import wandb
+
+                wandb.init(project=str(self.results_dir).split("/")[-1])
+
+        if not exists(self.GAN):
+            self.init_GAN()
+
+        G = self.GAN.G
+        D = self.GAN.D
+        D_aug = self.GAN.D_aug
+
+        # discriminator loss fn
+
+        self.set_data_src()
+
+        # prepare
+        G, D, D_aug, self.GAN.D_opt, self.GAN.G_opt, self.loader = self.accelerator.prepare(G, D, D_aug, self.GAN.D_opt,
+                                                                                            self.GAN.G_opt, self.loader)
+        self.loader = cycle(self.loader)
+
+        return G, D, D_aug
+
+    def train(self, G, D, D_aug):
+        assert exists(self.loader), 'You must first initialize the data source with `.set_data_src(<folder of images>)`'
+
+        self.GAN.train()
+        total_disc_loss = torch.zeros([], device=self.accelerator.device)
+        total_gen_loss = torch.zeros([], device=self.accelerator.device)
+
+        batch_size = math.ceil(self.batch_size / self.accelerator.num_processes)
+
+        image_size = self.GAN.image_size
+        latent_dim = self.GAN.latent_dim
+
+        aug_prob = default(self.aug_prob, 0)
+        aug_types = self.aug_types
+        aug_kwargs = {'prob': aug_prob, 'types': aug_types}
+
+        apply_gradient_penalty = self.steps % 4 == 0
+
+        # discriminator loss fn
+
+        if self.dual_contrast_loss:
+            D_loss_fn = dual_contrastive_loss
+        else:
+            D_loss_fn = hinge_loss
+
+        # train discriminator
+
+        self.GAN.D_opt.zero_grad()
+        for i in range(self.gradient_accumulate_every):
+            latents = torch.randn(batch_size, latent_dim, device=self.accelerator.device)
+            image_batch = next(self.loader)["image"]
+            image_batch.requires_grad_()
+
+            with torch.no_grad():
+                generated_images = G(latents)
+
+            fake_output, fake_output_32x32, _ = D_aug(generated_images, detach=True, **aug_kwargs)
+
+            real_output, real_output_32x32, real_aux_loss = D_aug(image_batch, calc_aux_loss=True, **aug_kwargs)
+
+            real_output_loss = real_output
+            fake_output_loss = fake_output
+
+            divergence = D_loss_fn(real_output_loss, fake_output_loss)
+            divergence_32x32 = D_loss_fn(real_output_32x32, fake_output_32x32)
+            disc_loss = divergence + divergence_32x32
+
+            aux_loss = real_aux_loss
+            disc_loss = disc_loss + aux_loss
+
+            if apply_gradient_penalty:
+                outputs = [real_output, real_output_32x32]
+                if self.accelerator.scaler is not None:
+                    outputs = list(map(self.accelerator.scaler.scale, outputs))
+
+                scaled_gradients = torch_grad(outputs=outputs, inputs=image_batch,
+                                              grad_outputs=list(
+                                                  map(lambda t: torch.ones(t.size(), device=self.accelerator.device),
+                                                      outputs)),
+                                              create_graph=True, retain_graph=True, only_inputs=True)[0]
+
+                inv_scale = 1.
+                if self.accelerator.scaler is not None:
+                    inv_scale = safe_div(1., self.accelerator.scaler.get_scale())
+
+                if inv_scale != float('inf'):
+                    gradients = scaled_gradients * inv_scale
+
+                    gradients = gradients.reshape(batch_size, -1)
+                    gp = self.gp_weight * ((gradients.norm(2, dim=1) - 1) ** 2).mean()
+
+                    if not torch.isnan(gp):
+                        disc_loss = disc_loss + gp
+                        self.last_gp_loss = gp.clone().detach().item()
+
+            # divide loss by gradient accumulation steps since gradients
+            # are accumulated for multiple backward passes in PyTorch
+            disc_loss = disc_loss / self.gradient_accumulate_every
+
+            disc_loss.register_hook(raise_if_nan)
+            self.accelerator.backward(disc_loss)
+            total_disc_loss += divergence
+
+        self.last_recon_loss = aux_loss.item()
+        self.d_loss = float(total_disc_loss.item() / self.gradient_accumulate_every)
+        self.GAN.D_opt.step()
+
+        # generator loss fn
+
+        if self.dual_contrast_loss:
+            G_loss_fn = dual_contrastive_loss
+            G_requires_calc_real = True
+        else:
+            G_loss_fn = gen_hinge_loss
+            G_requires_calc_real = False
+
+        # train generator
+
+        self.GAN.G_opt.zero_grad()
+
+        for i in range(self.gradient_accumulate_every):
+            latents = torch.randn(batch_size, latent_dim, device=self.accelerator.device)
+
+            if G_requires_calc_real:
+                image_batch = next(self.loader)["image"]
+                image_batch.requires_grad_()
+
+            generated_images = G(latents)
+
+            fake_output, fake_output_32x32, _ = D_aug(generated_images, **aug_kwargs)
+            real_output, real_output_32x32, _ = D_aug(image_batch, **aug_kwargs) if G_requires_calc_real else (
+                None, None, None)
+
+            loss = G_loss_fn(fake_output, real_output)
+            loss_32x32 = G_loss_fn(fake_output_32x32, real_output_32x32)
+
+            gen_loss = loss + loss_32x32
+
+            gen_loss = gen_loss / self.gradient_accumulate_every
+
+            gen_loss.register_hook(raise_if_nan)
+            self.accelerator.backward(gen_loss)
+            total_gen_loss += loss
+
+            # divide loss by gradient accumulation steps since gradients
+        # are accumulated for multiple backward passes in PyTorch
+        self.g_loss = float(total_gen_loss.item() / self.gradient_accumulate_every)
+        self.GAN.G_opt.step()
+
+        # calculate moving averages
+        if self.accelerator.is_main_process and self.steps % 10 == 0 and self.steps > 20000:
+            self.GAN.EMA()
+
+        if self.accelerator.is_main_process and self.steps <= 25000 and self.steps % 1000 == 2:
+            self.GAN.reset_parameter_averaging()
+
+        # save from NaN errors
+
+        if any(torch.isnan(l) for l in (total_gen_loss, total_disc_loss)):
+            print(f'NaN detected for generator or discriminator. Loading from checkpoint #{self.checkpoint_num}')
+            self.load(self.checkpoint_num)
+            raise NanException
+
+        del total_disc_loss
+        del total_gen_loss
+
+        # periodically save results
+
+        if self.accelerator.is_main_process:
+            if self.steps % self.save_every == 0:
+                self.save(self.checkpoint_num)
+
+                if self.push_to_hub:
+                    with tempfile.TemporaryDirectory() as temp_dir:
+                        self.GAN.push_to_hub(temp_dir, self.repo_url, config=self.GAN.config, skip_lfs_files=True)
+
+            if self.steps % self.evaluate_every == 0 or (self.steps % 100 == 0 and self.steps < 20000):
+                self.evaluate(floor(self.steps / self.evaluate_every), num_image_tiles=self.num_image_tiles)
+
+            if exists(self.calculate_fid_every) and self.steps % self.calculate_fid_every == 0 and self.steps != 0:
+                num_batches = math.ceil(self.calculate_fid_num_images / self.batch_size)
+                fid = self.calculate_fid(num_batches)
+                self.last_fid = fid
+
+                with open(str(self.results_dir / self.name / f'fid_scores.txt'), 'a') as f:
+                    f.write(f'{self.steps},{fid}\n')
+
+        self.steps += 1
+
+    @torch.no_grad()
+    def evaluate(self, num=0, num_image_tiles=4):
+        self.GAN.eval()
+
+        ext = self.image_extension
+        num_rows = num_image_tiles
+
+        latent_dim = self.GAN.latent_dim
+        image_size = self.GAN.image_size
+
+        # latents and noise
+
+        latents = torch.randn(num_rows ** 2, latent_dim, device=self.accelerator.device)
+
+        # regular
+
+        generated_images = self.generate_(self.GAN.G, latents)
+        file_name = str(self.results_dir / self.name / f'{str(num)}.{ext}')
+        save_image(generated_images, file_name, nrow=num_rows)
+
+        # moving averages
+
+        generated_images = self.generate_(self.GAN.GE.to(self.accelerator.device), latents)
+        file_name_ema = str(self.results_dir / self.name / f'{str(num)}-ema.{ext}')
+        save_image(generated_images, file_name_ema, nrow=num_rows)
+
+        if self.accelerator.is_local_main_process and self.wandb:
+            import wandb
+
+            wandb.log({'generated_examples': wandb.Image(str(file_name))})
+            wandb.log({'generated_examples_ema': wandb.Image(str(file_name_ema))})
+
+    @torch.no_grad()
+    def generate(self, num=0, num_image_tiles=4, checkpoint=None, types=['default', 'ema']):
+        self.GAN.eval()
+
+        latent_dim = self.GAN.latent_dim
+        dir_name = self.name + str('-generated-') + str(checkpoint)
+        dir_full = Path().absolute() / self.results_dir / dir_name
+        ext = self.image_extension
+
+        if not dir_full.exists():
+            os.mkdir(dir_full)
+
+        # regular
+        if 'default' in types:
+            for i in tqdm(range(num_image_tiles), desc='Saving generated default images'):
+                latents = torch.randn(1, latent_dim, device=self.accelerator.device)
+                generated_image = self.generate_(self.GAN.G, latents)
+                path = str(self.results_dir / dir_name / f'{str(num)}-{str(i)}.{ext}')
+                save_image(generated_image[0], path, nrow=1)
+
+        # moving averages
+        if 'ema' in types:
+            for i in tqdm(range(num_image_tiles), desc='Saving generated EMA images'):
+                latents = torch.randn(1, latent_dim, device=self.accelerator.device)
+                generated_image = self.generate_(self.GAN.GE, latents)
+                path = str(self.results_dir / dir_name / f'{str(num)}-{str(i)}-ema.{ext}')
+                save_image(generated_image[0], path, nrow=1)
+
+        return dir_full
+
+    @torch.no_grad()
+    def show_progress(self, num_images=4, types=['default', 'ema']):
+        checkpoints = self.get_checkpoints()
+        assert exists(checkpoints), 'cannot find any checkpoints to create a training progress video for'
+
+        dir_name = self.name + str('-progress')
+        dir_full = Path().absolute() / self.results_dir / dir_name
+        ext = self.image_extension
+        latents = None
+
+        zfill_length = math.ceil(math.log10(len(checkpoints)))
+
+        if not dir_full.exists():
+            os.mkdir(dir_full)
+
+        for checkpoint in tqdm(checkpoints, desc='Generating progress images'):
+            self.load(checkpoint, print_version=False)
+            self.GAN.eval()
+
+            if checkpoint == 0:
+                latents = torch.randn(num_images, self.GAN.latent_dim, self.accelerator.device)
+
+            # regular
+            if 'default' in types:
+                generated_image = self.generate_(self.GAN.G, latents)
+                path = str(self.results_dir / dir_name / f'{str(checkpoint).zfill(zfill_length)}.{ext}')
+                save_image(generated_image, path, nrow=num_images)
+
+            # moving averages
+            if 'ema' in types:
+                generated_image = self.generate_(self.GAN.GE, latents)
+                path = str(self.results_dir / dir_name / f'{str(checkpoint).zfill(zfill_length)}-ema.{ext}')
+                save_image(generated_image, path, nrow=num_images)
+
+    @torch.no_grad()
+    def calculate_fid(self, num_batches):
+        from pytorch_fid import fid_score
+        real_path = self.fid_dir / 'real'
+        fake_path = self.fid_dir / 'fake'
+
+        # remove any existing files used for fid calculation and recreate directories
+        if not real_path.exists() or self.clear_fid_cache:
+            rmtree(real_path, ignore_errors=True)
+            os.makedirs(real_path)
+
+            for batch_num in tqdm(range(num_batches), desc='calculating FID - saving reals'):
+                real_batch = next(self.loader)["image"]
+                for k, image in enumerate(real_batch.unbind(0)):
+                    ind = k + batch_num * self.batch_size
+                    save_image(image, real_path / f'{ind}.png')
+
+        # generate a bunch of fake images in results / name / fid_fake
+        rmtree(fake_path, ignore_errors=True)
+        os.makedirs(fake_path)
+
+        self.GAN.eval()
+        ext = self.image_extension
+
+        latent_dim = self.GAN.latent_dim
+        image_size = self.GAN.image_size
+
+        for batch_num in tqdm(range(num_batches), desc='calculating FID - saving generated'):
+            # latents and noise
+            latents = torch.randn(self.batch_size, latent_dim, device=self.accelerator.device)
+
+            # moving averages
+            generated_images = self.generate_(self.GAN.GE, latents)
+
+            for j, image in enumerate(generated_images.unbind(0)):
+                ind = j + batch_num * self.batch_size
+                save_image(image, str(fake_path / f'{str(ind)}-ema.{ext}'))
+
+        return fid_score.calculate_fid_given_paths([str(real_path), str(fake_path)], 256, latents.device, 2048)
+
+    @torch.no_grad()
+    def generate_(self, G, style, num_image_tiles=8):
+        generated_images = evaluate_in_chunks(self.batch_size, G, style)
+        return generated_images.clamp_(0., 1.)
+
+    @torch.no_grad()
+    def generate_interpolation(self, num=0, num_image_tiles=8, num_steps=100, save_frames=False):
+        self.GAN.eval()
+        ext = self.image_extension
+        num_rows = num_image_tiles
+
+        latent_dim = self.GAN.latent_dim
+        image_size = self.GAN.image_size
+
+        # latents and noise
+        latents_low = torch.randn(num_rows ** 2, latent_dim, device=self.accelerator.device)
+        latents_high = torch.randn(num_rows ** 2, latent_dim, device=self.accelerator.device)
+
+        ratios = torch.linspace(0., 8., num_steps)
+
+        frames = []
+        for ratio in tqdm(ratios):
+            interp_latents = slerp(ratio, latents_low, latents_high)
+            generated_images = self.generate_(self.GAN.GE, interp_latents)
+            images_grid = torchvision.utils.make_grid(generated_images, nrow=num_rows)
+            pil_image = transforms.ToPILImage()(images_grid.cpu())
+
+            if self.transparent:
+                background = Image.new('RGBA', pil_image.size, (255, 255, 255))
+                pil_image = Image.alpha_composite(background, pil_image)
+
+            frames.append(pil_image)
+
+        frames[0].save(str(self.results_dir / self.name / f'{str(num)}.gif'), save_all=True, append_images=frames[1:],
+                       duration=80, loop=0, optimize=True)
+
+        if save_frames:
+            folder_path = (self.results_dir / self.name / f'{str(num)}')
+            folder_path.mkdir(parents=True, exist_ok=True)
+            for ind, frame in enumerate(frames):
+                frame.save(str(folder_path / f'{str(ind)}.{ext}'))
+
+    def print_log(self):
+        data = [
+            ('G', self.g_loss),
+            ('D', self.d_loss),
+            ('GP', self.last_gp_loss),
+            ('SS', self.last_recon_loss),
+            ('FID', self.last_fid)
+        ]
+
+        data = [d for d in data if exists(d[1])]
+        log = ' | '.join(map(lambda n: f'{n[0]}: {n[1]:.2f}', data))
+        print(log)
+
+        if self.accelerator.is_local_main_process:
+            log_dict = {v[0]: v[1] for v in data}
+            if self.wandb:
+                import wandb
+
+                wandb.log(log_dict)
+
+    def model_name(self, num):
+        return str(self.models_dir / self.name / f'model_{num}.pt')
+
+    def init_folders(self):
+        (self.results_dir / self.name).mkdir(parents=True, exist_ok=True)
+        (self.models_dir / self.name).mkdir(parents=True, exist_ok=True)
+
+    def clear(self):
+        rmtree(str(self.models_dir / self.name), True)
+        rmtree(str(self.results_dir / self.name), True)
+        rmtree(str(self.fid_dir), True)
+        rmtree(str(self.config_path), True)
+        self.init_folders()
+
+    def save(self, num):
+        save_data = {
+            'GAN': self.GAN.state_dict(),
+        }
+
+        torch.save(save_data, self.model_name(num))
+        self.write_config()
+
+    def load(self, num=-1):
+        self.load_config()
+
+        name = num
+        if num == -1:
+            checkpoints = self.get_checkpoints()
+
+            if not exists(checkpoints):
+                return
+
+            name = checkpoints[-1]
+            print(f'continuing from previous epoch - {name}')
+
+        self.steps = name * self.save_every
+
+        load_data = torch.load(self.model_name(name))
+
+        try:
+            self.GAN.load_state_dict(load_data['GAN'])
+        except Exception as e:
+            print(
+                'unable to load save model. please try downgrading the package to the version specified by the saved model')
+            raise e
+
+    def get_checkpoints(self):
+        file_paths = [p for p in Path(self.models_dir / self.name).glob('model_*.pt')]
+        saved_nums = sorted(map(lambda x: int(x.stem.split('_')[1]), file_paths))
+
+        if len(saved_nums) == 0:
+            return None
+
+        return saved_nums
diff --git a/huggan/pytorch/metrics/README.md b/huggan/pytorch/metrics/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..78452abbe471835aba62fcfd97da223a9b319e0a
--- /dev/null
+++ b/huggan/pytorch/metrics/README.md
@@ -0,0 +1,39 @@
+# GAN metrics
+
+In order to track progress 📈 in (un)conditional image generation, a few quantitative metrics have been proposed. Below, we explain the most popular ones. For a more extensive overview, we refer the reader to [Borji, 2021](https://arxiv.org/abs/2103.09396) - which is an up-to-date version of [Borji, 2018](https://arxiv.org/abs/1802.03446). The TLDR is that, despite the use of many popular metrics, objective and comprehensive evaluation of generative models is still an open problem 🤷‍♂️.
+
+Quantitative metrics are of course just a proxy of image quality. The most widely used (Inception Score and FID) have several drawbacks [Barratt et al., 2018](https://arxiv.org/abs/1801.01973), [Sajjadi et al., 2018](https://arxiv.org/abs/1806.00035), [Kynkäänniemi et al., 2019](https://arxiv.org/abs/1904.06991).
+
+## Inception score
+
+The Inception score was proposed in [Salimans et al., 2016](https://arxiv.org/abs/1606.03498). The authors used a pre-trained Inceptionv3 neural net to classify the images generated by a GAN, and computed a score based on the class probablities of the neural net. The authors claimed that the score correlates well with subjective human evaluation. For an extensive explanation of the metric (as well as an implementation in Numpy and Keras), we refer the reader to [this blog post](https://machinelearningmastery.com/how-to-implement-the-inception-score-from-scratch-for-evaluating-generated-images/#:~:text=The%20Inception%20Score%2C%20or%20IS%20for%20short%2C%20is%20an%20objective,Improved%20Techniques%20for%20Training%20GANs.%E2%80%9D).
+
+## Fréchet Inception Distance (FID)
+
+The FID metric was proposed in [Heusel et al., 2018](https://arxiv.org/abs/1706.08500), and is currently the most widely used metric for evaluating image generation. Rather than only evaluating the generated images (as the Inception score), the FID metric compares the generated images to real images.
+
+The Fréchet distance meaures the distance between 2 multivariate Gaussian distributions. What does that mean? Concretely, the FID metric uses a pre-trained neural network (the same one as the one of the Inception score, Inceptionv3), and first forwards both real and generated images through it in order to get feature maps. Next, one computes statistics (namely, the mean and standard deviation) of the feature maps for both distributions (generated and real images). Finally, the distance between both distributions is computed based on these statistics.
+
+The FID metric assumes that feature maps of a pre-trained neural net extracted on real vs. fake images should be similar (the authors argue that this is a good quantitative metric for assessing image quality, correlating well with human judgement). 
+
+An important disadvantage of the FID metric is that is has an issue of generalization; a model that simply memorizes the training data can obtain a perfect score on these metrics [Razavi et al., 2019](https://arxiv.org/abs/1906.00446).
+
+Variants have been proposed for other modalities, such as the Fréchet Audio Distance [Kilgour et al., 2018](https://arxiv.org/abs/1812.08466) and the Fréchet Video Distance [Unterthiner et al., 2018](https://arxiv.org/abs/1812.01717). 
+
+The official implementation is in Tensorflow and can be found [here](https://github.com/bioinf-jku/TTUR). A PyTorch implementation can be found [here](https://github.com/mseitzer/pytorch-fid).
+
+## Clean FID
+
+In 2021, a paper by [Parmar et al.](https://arxiv.org/abs/2104.11222) indicated that the FID metric is often poorly computed, due to incorrect implementations of low-level image preprocessing (such as resizing of images) in popular frameworks such as PyTorch and TensorFlow. This can produce widely different values for the FID metric.
+
+The official implementation of the cleaner FID version can be found [here](https://github.com/GaParmar/clean-fid).
+
+Note that FID has many, many other variants including spatial FID (sFID), class-aware FID (CAFD) and conditional FID, Fast FID, Memorization-informed FID (MiFID), Unbiased FID, etc.
+
+## Precision and Recall
+
+Despite the FID metric being popular and correlating well with human evaluation, [Sajjadi et al., 2018](https://arxiv.org/abs/1806.00035) pointed out that, due to the fact that the FID score is just a scalar number, it is unable to distinguish between different failure cases. Two generative models could obtain the same FID score while generating images that look entirely different. Hence, the authors proposed a novel approach, defining precision (P) and recall (R) for distributions.
+
+Precision measures the similarity of generated instances to the real ones and recall measures the ability of a generator to synthesize all instances found in the training set. Hence, precision measures the quality and recall the coverage.
+
+These metrics were then further improved by [Kynkäänniemi et al., 2019](https://arxiv.org/abs/1904.06991).
diff --git a/huggan/pytorch/metrics/__init__.py b/huggan/pytorch/metrics/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/huggan/pytorch/metrics/fid_score.py b/huggan/pytorch/metrics/fid_score.py
new file mode 100644
index 0000000000000000000000000000000000000000..01d2ba3c25c05c30d13e2bdd6da98996b693fe35
--- /dev/null
+++ b/huggan/pytorch/metrics/fid_score.py
@@ -0,0 +1,80 @@
+# sources: 
+# https://www.kaggle.com/code/ibtesama/gan-in-pytorch-with-fid/notebook
+# https://github.com/mseitzer/pytorch-fid/blob/master/src/pytorch_fid/fid_score.py
+
+import numpy as np
+from scipy import linalg
+from torch.nn.functional import adaptive_avg_pool2d
+
+
+def calculate_activation_statistics(images, model, batch_size=128, dims=2048):
+    model.eval()
+    act = np.empty((len(images), dims))
+
+    batch = images
+    pred = model(batch)[0]
+
+    # If model output is not scalar, apply global spatial average pooling.
+    # This happens if you choose a dimensionality not equal 2048.
+    if pred.size(2) != 1 or pred.size(3) != 1:
+        pred = adaptive_avg_pool2d(pred, output_size=(1, 1))
+
+    act = pred.cpu().data.numpy().reshape(pred.size(0), -1)
+    
+    mu = np.mean(act, axis=0)
+    sigma = np.cov(act, rowvar=False)
+    return mu, sigma
+
+
+def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
+    """Numpy implementation of the Frechet Distance.
+    The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
+    and X_2 ~ N(mu_2, C_2) is
+            d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).
+    """
+
+    mu1 = np.atleast_1d(mu1)
+    mu2 = np.atleast_1d(mu2)
+
+    sigma1 = np.atleast_2d(sigma1)
+    sigma2 = np.atleast_2d(sigma2)
+
+    assert mu1.shape == mu2.shape, \
+        'Training and test mean vectors have different lengths'
+    assert sigma1.shape == sigma2.shape, \
+        'Training and test covariances have different dimensions'
+
+    diff = mu1 - mu2
+
+    
+    covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)
+    if not np.isfinite(covmean).all():
+        msg = ('fid calculation produces singular product; '
+               'adding %s to diagonal of cov estimates') % eps
+        print(msg)
+        offset = np.eye(sigma1.shape[0]) * eps
+        covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))
+
+    
+    if np.iscomplexobj(covmean):
+        if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
+            m = np.max(np.abs(covmean.imag))
+            raise ValueError('Imaginary component {}'.format(m))
+        covmean = covmean.real
+
+    tr_covmean = np.trace(covmean)
+
+    return (diff.dot(diff) + np.trace(sigma1) +
+            np.trace(sigma2) - 2 * tr_covmean)
+
+
+def calculate_fretchet(images_real, images_fake, model):
+    """Calculate the fretched distance."""
+     
+    # calculate statistics (mean + std)
+    mu_1, std_1 = calculate_activation_statistics(images_real, model)
+    mu_2, std_2 = calculate_activation_statistics(images_fake, model)
+    
+    # compute distance
+    fid_value = calculate_frechet_distance(mu_1, std_1, mu_2, std_2)
+    return fid_value
\ No newline at end of file
diff --git a/huggan/pytorch/metrics/inception.py b/huggan/pytorch/metrics/inception.py
new file mode 100644
index 0000000000000000000000000000000000000000..7af64d7a95dd4822c120e6787a1bab8cf1d77cf9
--- /dev/null
+++ b/huggan/pytorch/metrics/inception.py
@@ -0,0 +1,328 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision
+
+try:
+    from torchvision.models.utils import load_state_dict_from_url
+except ImportError:
+    from torch.utils.model_zoo import load_url as load_state_dict_from_url
+
+# Inception weights ported to Pytorch from
+# http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz
+FID_WEIGHTS_URL = 'https://github.com/mseitzer/pytorch-fid/releases/download/fid_weights/pt_inception-2015-12-05-6726825d.pth'  # noqa: E501
+
+
+class InceptionV3(nn.Module):
+    """Pretrained InceptionV3 network returning feature maps"""
+
+    # Index of default block of inception to return,
+    # corresponds to output of final average pooling
+    DEFAULT_BLOCK_INDEX = 3
+
+    # Maps feature dimensionality to their output blocks indices
+    BLOCK_INDEX_BY_DIM = {
+        64: 0,   # First max pooling features
+        192: 1,  # Second max pooling featurs
+        768: 2,  # Pre-aux classifier features
+        2048: 3  # Final average pooling features
+    }
+
+    def __init__(self,
+                 output_blocks=(DEFAULT_BLOCK_INDEX,),
+                 resize_input=True,
+                 normalize_input=True,
+                 requires_grad=False,
+                 use_fid_inception=True):
+        """Build pretrained InceptionV3
+
+        Parameters
+        ----------
+        output_blocks : list of int
+            Indices of blocks to return features of. Possible values are:
+                - 0: corresponds to output of first max pooling
+                - 1: corresponds to output of second max pooling
+                - 2: corresponds to output which is fed to aux classifier
+                - 3: corresponds to output of final average pooling
+        resize_input : bool
+            If true, bilinearly resizes input to width and height 299 before
+            feeding input to model. As the network without fully connected
+            layers is fully convolutional, it should be able to handle inputs
+            of arbitrary size, so resizing might not be strictly needed
+        normalize_input : bool
+            If true, scales the input from range (0, 1) to the range the
+            pretrained Inception network expects, namely (-1, 1)
+        requires_grad : bool
+            If true, parameters of the model require gradients. Possibly useful
+            for finetuning the network
+        use_fid_inception : bool
+            If true, uses the pretrained Inception model used in Tensorflow's
+            FID implementation. If false, uses the pretrained Inception model
+            available in torchvision. The FID Inception model has different
+            weights and a slightly different structure from torchvision's
+            Inception model. If you want to compute FID scores, you are
+            strongly advised to set this parameter to true to get comparable
+            results.
+        """
+        super(InceptionV3, self).__init__()
+
+        self.resize_input = resize_input
+        self.normalize_input = normalize_input
+        self.output_blocks = sorted(output_blocks)
+        self.last_needed_block = max(output_blocks)
+
+        assert self.last_needed_block <= 3, \
+            'Last possible output block index is 3'
+
+        self.blocks = nn.ModuleList()
+
+        if use_fid_inception:
+            inception = fid_inception_v3()
+        else:
+            inception = _inception_v3(pretrained=True)
+
+        # Block 0: input to maxpool1
+        block0 = [
+            inception.Conv2d_1a_3x3,
+            inception.Conv2d_2a_3x3,
+            inception.Conv2d_2b_3x3,
+            nn.MaxPool2d(kernel_size=3, stride=2)
+        ]
+        self.blocks.append(nn.Sequential(*block0))
+
+        # Block 1: maxpool1 to maxpool2
+        if self.last_needed_block >= 1:
+            block1 = [
+                inception.Conv2d_3b_1x1,
+                inception.Conv2d_4a_3x3,
+                nn.MaxPool2d(kernel_size=3, stride=2)
+            ]
+            self.blocks.append(nn.Sequential(*block1))
+
+        # Block 2: maxpool2 to aux classifier
+        if self.last_needed_block >= 2:
+            block2 = [
+                inception.Mixed_5b,
+                inception.Mixed_5c,
+                inception.Mixed_5d,
+                inception.Mixed_6a,
+                inception.Mixed_6b,
+                inception.Mixed_6c,
+                inception.Mixed_6d,
+                inception.Mixed_6e,
+            ]
+            self.blocks.append(nn.Sequential(*block2))
+
+        # Block 3: aux classifier to final avgpool
+        if self.last_needed_block >= 3:
+            block3 = [
+                inception.Mixed_7a,
+                inception.Mixed_7b,
+                inception.Mixed_7c,
+                nn.AdaptiveAvgPool2d(output_size=(1, 1))
+            ]
+            self.blocks.append(nn.Sequential(*block3))
+
+        for param in self.parameters():
+            param.requires_grad = requires_grad
+
+    def forward(self, inp):
+        """Get Inception feature maps
+
+        Parameters
+        ----------
+        inp : torch.autograd.Variable
+            Input tensor of shape Bx3xHxW. Values are expected to be in
+            range (0, 1)
+
+        Returns
+        -------
+        List of torch.autograd.Variable, corresponding to the selected output
+        block, sorted ascending by index
+        """
+        outp = []
+        x = inp
+
+        if self.resize_input:
+            x = F.interpolate(x,
+                              size=(299, 299),
+                              mode='bilinear',
+                              align_corners=False)
+
+        if self.normalize_input:
+            x = 2 * x - 1  # Scale from range (0, 1) to range (-1, 1)
+
+        for idx, block in enumerate(self.blocks):
+            x = block(x)
+            if idx in self.output_blocks:
+                outp.append(x)
+
+            if idx == self.last_needed_block:
+                break
+
+        return outp
+
+
+def _inception_v3(*args, **kwargs):
+    """Wraps `torchvision.models.inception_v3`
+
+    Skips default weight inititialization if supported by torchvision version.
+    See https://github.com/mseitzer/pytorch-fid/issues/28.
+    """
+    try:
+        version = tuple(map(int, torchvision.__version__.split('.')[:2]))
+    except ValueError:
+        # Just a caution against weird version strings
+        version = (0,)
+
+    if version >= (0, 6):
+        kwargs['init_weights'] = False
+
+    return torchvision.models.inception_v3(*args, **kwargs)
+
+
+def fid_inception_v3():
+    """Build pretrained Inception model for FID computation
+
+    The Inception model for FID computation uses a different set of weights
+    and has a slightly different structure than torchvision's Inception.
+
+    This method first constructs torchvision's Inception and then patches the
+    necessary parts that are different in the FID Inception model.
+    """
+    inception = _inception_v3(num_classes=1008,
+                              aux_logits=False,
+                              pretrained=False)
+    inception.Mixed_5b = FIDInceptionA(192, pool_features=32)
+    inception.Mixed_5c = FIDInceptionA(256, pool_features=64)
+    inception.Mixed_5d = FIDInceptionA(288, pool_features=64)
+    inception.Mixed_6b = FIDInceptionC(768, channels_7x7=128)
+    inception.Mixed_6c = FIDInceptionC(768, channels_7x7=160)
+    inception.Mixed_6d = FIDInceptionC(768, channels_7x7=160)
+    inception.Mixed_6e = FIDInceptionC(768, channels_7x7=192)
+    inception.Mixed_7b = FIDInceptionE_1(1280)
+    inception.Mixed_7c = FIDInceptionE_2(2048)
+
+    state_dict = load_state_dict_from_url(FID_WEIGHTS_URL, progress=True)
+    inception.load_state_dict(state_dict)
+    return inception
+
+
+class FIDInceptionA(torchvision.models.inception.InceptionA):
+    """InceptionA block patched for FID computation"""
+    def __init__(self, in_channels, pool_features):
+        super(FIDInceptionA, self).__init__(in_channels, pool_features)
+
+    def forward(self, x):
+        branch1x1 = self.branch1x1(x)
+
+        branch5x5 = self.branch5x5_1(x)
+        branch5x5 = self.branch5x5_2(branch5x5)
+
+        branch3x3dbl = self.branch3x3dbl_1(x)
+        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
+        branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
+
+        # Patch: Tensorflow's average pool does not use the padded zero's in
+        # its average calculation
+        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1,
+                                   count_include_pad=False)
+        branch_pool = self.branch_pool(branch_pool)
+
+        outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]
+        return torch.cat(outputs, 1)
+
+
+class FIDInceptionC(torchvision.models.inception.InceptionC):
+    """InceptionC block patched for FID computation"""
+    def __init__(self, in_channels, channels_7x7):
+        super(FIDInceptionC, self).__init__(in_channels, channels_7x7)
+
+    def forward(self, x):
+        branch1x1 = self.branch1x1(x)
+
+        branch7x7 = self.branch7x7_1(x)
+        branch7x7 = self.branch7x7_2(branch7x7)
+        branch7x7 = self.branch7x7_3(branch7x7)
+
+        branch7x7dbl = self.branch7x7dbl_1(x)
+        branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
+        branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
+        branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
+        branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)
+
+        # Patch: Tensorflow's average pool does not use the padded zero's in
+        # its average calculation
+        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1,
+                                   count_include_pad=False)
+        branch_pool = self.branch_pool(branch_pool)
+
+        outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]
+        return torch.cat(outputs, 1)
+
+
+class FIDInceptionE_1(torchvision.models.inception.InceptionE):
+    """First InceptionE block patched for FID computation"""
+    def __init__(self, in_channels):
+        super(FIDInceptionE_1, self).__init__(in_channels)
+
+    def forward(self, x):
+        branch1x1 = self.branch1x1(x)
+
+        branch3x3 = self.branch3x3_1(x)
+        branch3x3 = [
+            self.branch3x3_2a(branch3x3),
+            self.branch3x3_2b(branch3x3),
+        ]
+        branch3x3 = torch.cat(branch3x3, 1)
+
+        branch3x3dbl = self.branch3x3dbl_1(x)
+        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
+        branch3x3dbl = [
+            self.branch3x3dbl_3a(branch3x3dbl),
+            self.branch3x3dbl_3b(branch3x3dbl),
+        ]
+        branch3x3dbl = torch.cat(branch3x3dbl, 1)
+
+        # Patch: Tensorflow's average pool does not use the padded zero's in
+        # its average calculation
+        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1,
+                                   count_include_pad=False)
+        branch_pool = self.branch_pool(branch_pool)
+
+        outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
+        return torch.cat(outputs, 1)
+
+
+class FIDInceptionE_2(torchvision.models.inception.InceptionE):
+    """Second InceptionE block patched for FID computation"""
+    def __init__(self, in_channels):
+        super(FIDInceptionE_2, self).__init__(in_channels)
+
+    def forward(self, x):
+        branch1x1 = self.branch1x1(x)
+
+        branch3x3 = self.branch3x3_1(x)
+        branch3x3 = [
+            self.branch3x3_2a(branch3x3),
+            self.branch3x3_2b(branch3x3),
+        ]
+        branch3x3 = torch.cat(branch3x3, 1)
+
+        branch3x3dbl = self.branch3x3dbl_1(x)
+        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
+        branch3x3dbl = [
+            self.branch3x3dbl_3a(branch3x3dbl),
+            self.branch3x3dbl_3b(branch3x3dbl),
+        ]
+        branch3x3dbl = torch.cat(branch3x3dbl, 1)
+
+        # Patch: The FID Inception model uses max pooling instead of average
+        # pooling. This is likely an error in this specific Inception
+        # implementation, as other Inception models use average pooling here
+        # (which matches the description in the paper).
+        branch_pool = F.max_pool2d(x, kernel_size=3, stride=1, padding=1)
+        branch_pool = self.branch_pool(branch_pool)
+
+        outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
+        return torch.cat(outputs, 1)
\ No newline at end of file
diff --git a/huggan/pytorch/pix2pix/README.md b/huggan/pytorch/pix2pix/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..72bc7dee7d61652a889357ccf57888057b289c3c
--- /dev/null
+++ b/huggan/pytorch/pix2pix/README.md
@@ -0,0 +1,91 @@
+# Train Pix2pix on your custom data
+
+This folder contains a script to train [pix2pix](https://arxiv.org/abs/1611.07004) for conditional image generation, leveraging the [Hugging Face](https://huggingface.co/) ecosystem for processing data and pushing the model to the Hub.
+
+The script leverages 🤗 Datasets for loading and processing data, and 🤗 Accelerate for instantly running on CPU, single, multi-GPUs or TPU, also supporting fp16/mixed precision.
+
+<p align="center">
+    <img src="https://raw.githubusercontent.com/huggingface/community-events/main/huggan/assets/pix2pix_maps.png" alt="drawing" width="300"/>
+</p>
+
+Pix2pix trained on the [huggan/maps](https://huggingface.co/datasets/huggan/maps) dataset to translate satellite images into maps à la Google Maps. First row: input, second row: prediction, third row: ground truth.
+
+## Launching the script
+
+To train the model with the default parameters (200 epochs, 256x256 images, etc.) on [huggan/facades](https://huggingface.co/datasets/huggan/facades) on your environment, first run:
+
+```bash
+accelerate config
+```
+
+and answer the questions asked about your environment. Next, launch the script as follows: 
+
+```
+accelerate launch train.py
+```
+
+This will create local "images" and "saved_models" directories, containing generated images and saved checkpoints over the course of the training.
+
+To train on another dataset available on the hub, simply do (for instance):
+
+```
+accelerate launch train.py --dataset huggan/night2day
+```
+
+Make sure to pick a dataset which has "imageA" and "imageB" columns defined. One can always tweak the script in case the column names are different.
+
+In case you'd like to tweak the script to your liking, first fork the "community-events" [repo](https://github.com/huggingface/community-events) (see the button on the top right), then clone it locally:
+
+```bash
+git clone https://github.com/<your Github username>/community-events.git
+```
+
+and edit to your liking.
+
+## Training on your own data
+
+You can of course also train on your own images. For this, one can leverage Datasets' [ImageFolder](https://huggingface.co/docs/datasets/v2.0.0/en/image_process#imagefolder). Make sure to authenticate with the hub first, either by running the `huggingface-cli login` command in a terminal, or the following in case you're working in a notebook:
+
+```python
+from huggingface_hub import notebook_login
+
+notebook_login()
+```
+
+Next, run the following in a notebook/script:
+
+```python
+from datasets import load_dataset
+
+# first: load dataset
+# option 1: from local folder
+dataset = load_dataset("imagefolder", data_dir="path_to_folder")
+# option 2: from remote URL (e.g. a zip file)
+dataset = load_dataset("imagefolder", data_files="URL to .zip file")
+
+# optional: remove "label" column, in case there are no subcategories
+dataset['train'] = dataset['train'].remove_columns(column_names="label")
+
+# next: push to the hub (assuming git-LFS is installed)
+dataset.push_to_hub("huggan/my-awesome-dataset")
+```
+
+You can then simply pass the name of the dataset to the script:
+
+```
+accelerate launch train.py --dataset huggan/my-awesome-dataset
+```
+
+## Pushing model to the Hub
+
+You can push your trained generator to the hub during training by specifying the `push_to_hub` flag, along with a `model_name`. 
+
+```bash
+accelerate launch train.py --push_to_hub --model_name pix2pix-facades
+```
+
+This is made possible by making the generator inherit from `PyTorchModelHubMixin` available in the `huggingface_hub` library. 
+
+# Citation
+
+This repo is entirely based on Erik Linder-Norén's [PyTorch-GAN repo](https://github.com/eriklindernoren/PyTorch-GAN), but with added HuggingFace goodies.
diff --git a/huggan/pytorch/pix2pix/__init__.py b/huggan/pytorch/pix2pix/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/huggan/pytorch/pix2pix/modeling_pix2pix.py b/huggan/pytorch/pix2pix/modeling_pix2pix.py
new file mode 100644
index 0000000000000000000000000000000000000000..71d4efe3380139c9231bf28d619d0742a48f9e27
--- /dev/null
+++ b/huggan/pytorch/pix2pix/modeling_pix2pix.py
@@ -0,0 +1,150 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright (c) 2022 Erik Linder-Norén and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions.
+
+import torch.nn as nn
+import torch.nn.functional as F
+import torch
+
+from huggan.pytorch.huggan_mixin import HugGANModelHubMixin
+
+
+def weights_init_normal(m):
+    classname = m.__class__.__name__
+    if classname.find("Conv") != -1:
+        torch.nn.init.normal_(m.weight.data, 0.0, 0.02)
+    elif classname.find("BatchNorm2d") != -1:
+        torch.nn.init.normal_(m.weight.data, 1.0, 0.02)
+        torch.nn.init.constant_(m.bias.data, 0.0)
+
+
+##############################
+#           U-NET
+##############################
+
+
+class UNetDown(nn.Module):
+    def __init__(self, in_size, out_size, normalize=True, dropout=0.0):
+        super(UNetDown, self).__init__()
+        layers = [nn.Conv2d(in_size, out_size, 4, 2, 1, bias=False)]
+        if normalize:
+            layers.append(nn.InstanceNorm2d(out_size))
+        layers.append(nn.LeakyReLU(0.2))
+        if dropout:
+            layers.append(nn.Dropout(dropout))
+        self.model = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.model(x)
+
+
+class UNetUp(nn.Module):
+    def __init__(self, in_size, out_size, dropout=0.0):
+        super(UNetUp, self).__init__()
+        layers = [
+            nn.ConvTranspose2d(in_size, out_size, 4, 2, 1, bias=False),
+            nn.InstanceNorm2d(out_size),
+            nn.ReLU(inplace=True),
+        ]
+        if dropout:
+            layers.append(nn.Dropout(dropout))
+
+        self.model = nn.Sequential(*layers)
+
+    def forward(self, x, skip_input):
+        x = self.model(x)
+        x = torch.cat((x, skip_input), 1)
+
+        return x
+
+
+class GeneratorUNet(nn.Module, HugGANModelHubMixin):
+    def __init__(self, in_channels=3, out_channels=3):
+        super(GeneratorUNet, self).__init__()
+
+        self.down1 = UNetDown(in_channels, 64, normalize=False)
+        self.down2 = UNetDown(64, 128)
+        self.down3 = UNetDown(128, 256)
+        self.down4 = UNetDown(256, 512, dropout=0.5)
+        self.down5 = UNetDown(512, 512, dropout=0.5)
+        self.down6 = UNetDown(512, 512, dropout=0.5)
+        self.down7 = UNetDown(512, 512, dropout=0.5)
+        self.down8 = UNetDown(512, 512, normalize=False, dropout=0.5)
+
+        self.up1 = UNetUp(512, 512, dropout=0.5)
+        self.up2 = UNetUp(1024, 512, dropout=0.5)
+        self.up3 = UNetUp(1024, 512, dropout=0.5)
+        self.up4 = UNetUp(1024, 512, dropout=0.5)
+        self.up5 = UNetUp(1024, 256)
+        self.up6 = UNetUp(512, 128)
+        self.up7 = UNetUp(256, 64)
+
+        self.final = nn.Sequential(
+            nn.Upsample(scale_factor=2),
+            nn.ZeroPad2d((1, 0, 1, 0)),
+            nn.Conv2d(128, out_channels, 4, padding=1),
+            nn.Tanh(),
+        )
+
+    def forward(self, x):
+        # U-Net generator with skip connections from encoder to decoder
+        d1 = self.down1(x)
+        d2 = self.down2(d1)
+        d3 = self.down3(d2)
+        d4 = self.down4(d3)
+        d5 = self.down5(d4)
+        d6 = self.down6(d5)
+        d7 = self.down7(d6)
+        d8 = self.down8(d7)
+        u1 = self.up1(d8, d7)
+        u2 = self.up2(u1, d6)
+        u3 = self.up3(u2, d5)
+        u4 = self.up4(u3, d4)
+        u5 = self.up5(u4, d3)
+        u6 = self.up6(u5, d2)
+        u7 = self.up7(u6, d1)
+
+        return self.final(u7)
+
+
+##############################
+#        Discriminator
+##############################
+
+
+class Discriminator(nn.Module):
+    def __init__(self, in_channels=3):
+        super(Discriminator, self).__init__()
+
+        def discriminator_block(in_filters, out_filters, normalization=True):
+            """Returns downsampling layers of each discriminator block"""
+            layers = [nn.Conv2d(in_filters, out_filters, 4, stride=2, padding=1)]
+            if normalization:
+                layers.append(nn.InstanceNorm2d(out_filters))
+            layers.append(nn.LeakyReLU(0.2, inplace=True))
+            return layers
+
+        self.model = nn.Sequential(
+            *discriminator_block(in_channels * 2, 64, normalization=False),
+            *discriminator_block(64, 128),
+            *discriminator_block(128, 256),
+            *discriminator_block(256, 512),
+            nn.ZeroPad2d((1, 0, 1, 0)),
+            nn.Conv2d(512, 1, 4, padding=1, bias=False)
+        )
+
+    def forward(self, img_A, img_B):
+        # Concatenate image and condition image by channels to produce input
+        img_input = torch.cat((img_A, img_B), 1)
+        return self.model(img_input)
\ No newline at end of file
diff --git a/huggan/pytorch/pix2pix/train.py b/huggan/pytorch/pix2pix/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..67ea3ce73c35f709136e5a13c775df4d40472abf
--- /dev/null
+++ b/huggan/pytorch/pix2pix/train.py
@@ -0,0 +1,306 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright (c) 2022 Erik Linder-Norén and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions.
+
+import argparse
+import os
+from pathlib import Path
+import numpy as np
+import time
+import datetime
+import sys
+import tempfile
+
+from torchvision.transforms import Compose, Resize, ToTensor, Normalize, RandomVerticalFlip
+from torchvision.utils import save_image
+
+from PIL import Image
+
+from torch.utils.data import DataLoader
+
+from modeling_pix2pix import GeneratorUNet, Discriminator
+
+from datasets import load_dataset
+
+from accelerate import Accelerator
+
+import torch.nn as nn
+import torch
+
+from huggan.utils.hub import get_full_repo_name
+from huggingface_hub import create_repo
+
+
+def parse_args(args=None):
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--dataset", type=str, default="huggan/facades", help="Dataset to use")
+    parser.add_argument("--epoch", type=int, default=0, help="epoch to start training from")
+    parser.add_argument("--n_epochs", type=int, default=200, help="number of epochs of training")
+    parser.add_argument("--batch_size", type=int, default=1, help="size of the batches")
+    parser.add_argument("--lr", type=float, default=0.0002, help="adam: learning rate")
+    parser.add_argument("--b1", type=float, default=0.5, help="adam: decay of first order momentum of gradient")
+    parser.add_argument("--b2", type=float, default=0.999, help="adam: decay of first order momentum of gradient")
+    parser.add_argument("--decay_epoch", type=int, default=100, help="epoch from which to start lr decay")
+    parser.add_argument("--n_cpu", type=int, default=8, help="number of cpu threads to use during batch generation")
+    parser.add_argument("--image_size", type=int, default=256, help="size of images for training")
+    parser.add_argument("--channels", type=int, default=3, help="number of image channels")
+    parser.add_argument(
+        "--sample_interval", type=int, default=500, help="interval between sampling of images from generators"
+    )
+    parser.add_argument("--checkpoint_interval", type=int, default=-1, help="interval between model checkpoints")
+    parser.add_argument("--fp16", action="store_true", help="If passed, will use FP16 training.")
+    parser.add_argument(
+        "--mixed_precision",
+        type=str,
+        default="no",
+        choices=["no", "fp16", "bf16"],
+        help="Whether to use mixed precision. Choose"
+        "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
+        "and an Nvidia Ampere GPU.",
+    )
+    parser.add_argument("--cpu", action="store_true", help="If passed, will train on the CPU.")
+    parser.add_argument(
+        "--push_to_hub",
+        action="store_true",
+        help="Whether to push the model to the HuggingFace hub after training.",
+        )
+    parser.add_argument(
+        "--model_name",
+        required="--push_to_hub" in sys.argv,
+        type=str,
+        help="Name of the model on the hub.",
+    )
+    parser.add_argument(
+        "--organization_name",
+        required=False,
+        default="huggan",
+        type=str,
+        help="Organization name to push to, in case args.push_to_hub is specified.",
+    )
+    return parser.parse_args(args=args)
+
+# Custom weights initialization called on Generator and Discriminator
+def weights_init_normal(m):
+    classname = m.__class__.__name__
+    if classname.find("Conv") != -1:
+        torch.nn.init.normal_(m.weight.data, 0.0, 0.02)
+    elif classname.find("BatchNorm2d") != -1:
+        torch.nn.init.normal_(m.weight.data, 1.0, 0.02)
+        torch.nn.init.constant_(m.bias.data, 0.0)
+
+def training_function(config, args):
+    accelerator = Accelerator(fp16=args.fp16, cpu=args.cpu, mixed_precision=args.mixed_precision)
+
+    os.makedirs("images/%s" % args.dataset, exist_ok=True)
+    os.makedirs("saved_models/%s" % args.dataset, exist_ok=True)
+    
+    repo_name = get_full_repo_name(args.model_name, args.organization_name)
+    if args.push_to_hub:
+        if accelerator.is_main_process:
+            repo_url = create_repo(repo_name, exist_ok=True)
+    # Loss functions
+    criterion_GAN = torch.nn.MSELoss()
+    criterion_pixelwise = torch.nn.L1Loss()
+
+    # Loss weight of L1 pixel-wise loss between translated image and real image
+    lambda_pixel = 100
+
+    # Calculate output of image discriminator (PatchGAN)
+    patch = (1, args.image_size // 2 ** 4, args.image_size // 2 ** 4)
+
+    # Initialize generator and discriminator
+    generator = GeneratorUNet()
+    discriminator = Discriminator()
+
+    if args.epoch != 0:
+        # Load pretrained models
+        generator.load_state_dict(torch.load("saved_models/%s/generator_%d.pth" % (args.dataset, args.epoch)))
+        discriminator.load_state_dict(torch.load("saved_models/%s/discriminator_%d.pth" % (args.dataset, args.epoch)))
+    else:
+        # Initialize weights
+        generator.apply(weights_init_normal)
+        discriminator.apply(weights_init_normal)
+
+    # Optimizers
+    optimizer_G = torch.optim.Adam(generator.parameters(), lr=args.lr, betas=(args.b1, args.b2))
+    optimizer_D = torch.optim.Adam(discriminator.parameters(), lr=args.lr, betas=(args.b1, args.b2))
+
+    # Configure dataloaders
+    transform = Compose(
+            [
+                Resize((args.image_size, args.image_size), Image.BICUBIC),
+                ToTensor(),
+                Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
+            ]
+        )
+
+    def transforms(examples):
+        # random vertical flip
+        imagesA = []
+        imagesB = []
+        for imageA, imageB in zip(examples['imageA'], examples['imageB']):
+            if np.random.random() < 0.5:
+                imageA = Image.fromarray(np.array(imageA)[:, ::-1, :], "RGB")
+                imageB = Image.fromarray(np.array(imageB)[:, ::-1, :], "RGB")
+            imagesA.append(imageA)
+            imagesB.append(imageB)  
+        
+        # transforms
+        examples["A"] = [transform(image.convert("RGB")) for image in imagesA]
+        examples["B"] = [transform(image.convert("RGB")) for image in imagesB]
+
+        del examples["imageA"]
+        del examples["imageB"]
+
+        return examples
+
+    dataset = load_dataset(args.dataset)
+    transformed_dataset = dataset.with_transform(transforms)
+
+    splits = transformed_dataset['train'].train_test_split(test_size=0.1)
+    train_ds = splits['train']
+    val_ds = splits['test']
+
+    dataloader = DataLoader(train_ds, shuffle=True, batch_size=args.batch_size, num_workers=args.n_cpu)
+    val_dataloader = DataLoader(val_ds, batch_size=10, shuffle=True, num_workers=1)
+
+    def sample_images(batches_done, accelerator):
+        """Saves a generated sample from the validation set"""
+        batch = next(iter(val_dataloader))
+        real_A = batch["A"]
+        real_B = batch["B"]
+        fake_B = generator(real_A)
+        img_sample = torch.cat((real_A.data, fake_B.data, real_B.data), -2)
+        if accelerator.is_main_process:
+            save_image(img_sample, "images/%s/%s.png" % (args.dataset, batches_done), nrow=5, normalize=True)
+
+    generator, discriminator, optimizer_G, optimizer_D, dataloader, val_dataloader = accelerator.prepare(generator, discriminator, optimizer_G, optimizer_D, dataloader, val_dataloader)
+
+    # ----------
+    #  Training
+    # ----------
+
+    prev_time = time.time()
+
+    for epoch in range(args.epoch, args.n_epochs):
+        print("Epoch:", epoch)
+        for i, batch in enumerate(dataloader):
+
+            # Model inputs
+            real_A = batch["A"]
+            real_B = batch["B"]
+            
+            # Adversarial ground truths
+            valid = torch.ones((real_A.size(0), *patch), device=accelerator.device)
+            fake = torch.zeros((real_A.size(0), *patch), device=accelerator.device)
+
+            # ------------------
+            #  Train Generators
+            # ------------------
+
+            optimizer_G.zero_grad()
+
+            # GAN loss
+            fake_B = generator(real_A)
+            pred_fake = discriminator(fake_B, real_A)
+            loss_GAN = criterion_GAN(pred_fake, valid)
+            # Pixel-wise loss
+            loss_pixel = criterion_pixelwise(fake_B, real_B)
+
+            # Total loss
+            loss_G = loss_GAN + lambda_pixel * loss_pixel
+
+            accelerator.backward(loss_G)
+
+            optimizer_G.step()
+
+            # ---------------------
+            #  Train Discriminator
+            # ---------------------
+
+            optimizer_D.zero_grad()
+
+            # Real loss
+            pred_real = discriminator(real_B, real_A)
+            loss_real = criterion_GAN(pred_real, valid)
+
+            # Fake loss
+            pred_fake = discriminator(fake_B.detach(), real_A)
+            loss_fake = criterion_GAN(pred_fake, fake)
+
+            # Total loss
+            loss_D = 0.5 * (loss_real + loss_fake)
+
+            accelerator.backward(loss_D)
+            optimizer_D.step()
+
+            # --------------
+            #  Log Progress
+            # --------------
+
+            # Determine approximate time left
+            batches_done = epoch * len(dataloader) + i
+            batches_left = args.n_epochs * len(dataloader) - batches_done
+            time_left = datetime.timedelta(seconds=batches_left * (time.time() - prev_time))
+            prev_time = time.time()
+
+            # Print log
+            sys.stdout.write(
+                "\r[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f, pixel: %f, adv: %f] ETA: %s"
+                % (
+                    epoch,
+                    args.n_epochs,
+                    i,
+                    len(dataloader),
+                    loss_D.item(),
+                    loss_G.item(),
+                    loss_pixel.item(),
+                    loss_GAN.item(),
+                    time_left,
+                )
+            )
+
+            # If at sample interval save image
+            if batches_done % args.sample_interval == 0:
+                sample_images(batches_done, accelerator)
+
+        if args.checkpoint_interval != -1 and epoch % args.checkpoint_interval == 0:
+            if accelerator.is_main_process:
+                unwrapped_generator = accelerator.unwrap_model(generator)
+                unwrapped_discriminator = accelerator.unwrap_model(discriminator)
+                # Save model checkpoints
+                torch.save(unwrapped_generator.state_dict(), "saved_models/%s/generator_%d.pth" % (args.dataset, epoch))
+                torch.save(unwrapped_discriminator.state_dict(), "saved_models/%s/discriminator_%d.pth" % (args.dataset, epoch))
+
+        # Optionally push to hub
+        if args.push_to_hub:
+            if accelerator.is_main_process:
+                with tempfile.TemporaryDirectory() as temp_dir:
+                    unwrapped_generator = accelerator.unwrap_model(generator)
+                    unwrapped_generator.push_to_hub(
+                        repo_path_or_name=temp_dir,
+                        repo_url=repo_url,
+                        commit_message=f"Training in progress, epoch {epoch}",
+                        skip_lfs_files=True
+                    )
+
+def main():
+    args = parse_args()
+    print(args)
+
+    training_function({}, args)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/huggan/tensorflow/dcgan/README.md b/huggan/tensorflow/dcgan/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..e6eb62316b09f08f9c17b03e3631bd512b823d79
--- /dev/null
+++ b/huggan/tensorflow/dcgan/README.md
@@ -0,0 +1,50 @@
+## Train DCGAN on your custom data
+This folder contains a script to train DCGAN for unconditional image generation, leveraging the Hugging Face ecosystem for processing your data and pushing the model to the Hub.
+
+The script leverages 🤗 [Datasets](https://huggingface.co/docs/datasets/index) for loading and processing data, and TensorFlow for training the model and 🤗 [Hub](https://huggingface.co/) for hosting it.
+
+## Launching the script
+You can simply run `python train.py --num_channels 1` with the default parameters. It will download the [MNIST](https://huggingface.co/datasets/mnist) dataset, preprocess it and train a model on it, will save results after each epoch in a local directory and push the model to the 🤗 Hub.
+
+To train on another dataset available on the hub, simply do (for instance):
+
+```bash
+python train.py --dataset cifar10
+```
+
+## Training on your own data
+You can of course also train on your own images. For this, one can leverage Datasets' [ImageFolder](https://huggingface.co/docs/datasets/v2.0.0/en/image_process#imagefolder). Make sure to authenticate with the hub first, by running the huggingface-cli login command in a terminal, or the following in case you're working in a notebook:
+
+```python
+from huggingface_hub import notebook_login
+
+notebook_login()
+```
+
+Next, run the following in a notebook/script:
+
+```python
+from datasets import load_dataset
+
+# first: load dataset
+# option 1: from local folder
+dataset = load_dataset("imagefolder", data_dir="path_to_folder")
+# option 2: from remote URL (e.g. a zip file)
+dataset = load_dataset("imagefolder", data_files="URL to .zip file")
+
+# next: push to the hub (assuming git-LFS is installed)
+dataset.push_to_hub("huggan/my-awesome-dataset")
+# You can then simply pass the name of the dataset to the script:
+
+python train.py --dataset huggan/my-awesome-dataset
+```
+
+## Pushing model to the Hub
+
+For this you can use `push_to_hub_keras` which generates a card for your model with training metrics, plot of the architecture and hyperparameters. For this, specify `--output_dir` and `--model_name` and use the `--push_to_hub` flag like so:
+```bash
+python train.py --push_to_hub --output_dir /output --model_name awesome_gan_model
+```
+
+## Citation
+This repo is entirely based on [TensorFlow's official DCGAN tutorial](https://www.tensorflow.org/tutorials/generative/dcgan), but with added HuggingFace goodies.
diff --git a/huggan/tensorflow/dcgan/__init__.py b/huggan/tensorflow/dcgan/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/huggan/tensorflow/dcgan/requirements.txt b/huggan/tensorflow/dcgan/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..462542f1c1668f5da3a3469999b12f91f6898acc
--- /dev/null
+++ b/huggan/tensorflow/dcgan/requirements.txt
@@ -0,0 +1,2 @@
+transformers
+datasets
diff --git a/huggan/tensorflow/dcgan/train.py b/huggan/tensorflow/dcgan/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..32554915526f782a43c65013dbb0709dd23cbdc8
--- /dev/null
+++ b/huggan/tensorflow/dcgan/train.py
@@ -0,0 +1,225 @@
+
+import tensorflow as tf
+import matplotlib.pyplot as plt
+import numpy as np
+import tensorflow as tf
+from pathlib import Path
+import os
+import PIL
+from tqdm.auto import tqdm
+import argparse
+
+from tensorflow.keras import layers
+
+from datasets import load_dataset
+from transformers import DefaultDataCollator
+from huggingface_hub import push_to_hub_keras
+
+
+def parse_args(args=None):
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--dataset", type=str, default="mnist", help="Dataset to load from the HuggingFace hub.")
+    parser.add_argument("--batch_size", type=int, default=128, help="Batch size to use during training")
+    parser.add_argument("--number_of_examples_to_generate", type=int, default=4, help="Number of examples to be generated in inference mode")
+    parser.add_argument(
+        "--generator_hidden_size",
+        type=int,
+        default=28,
+        help="Hidden size of the generator's feature maps.",
+    )
+    parser.add_argument("--latent_dim", type=int, default=100, help="Dimensionality of the latent space.")
+
+    parser.add_argument(
+        "--discriminator_hidden_size",
+        type=int,
+        default=28,
+        help="Hidden size of the discriminator's feature maps.",
+    )
+    parser.add_argument(
+        "--image_size",
+        type=int,
+        default=28,
+        help="Spatial size to use when resizing images for training.",
+    )
+    parser.add_argument(
+        "--num_channels",
+        type=int,
+        default=3,
+        help="Number of channels in the training images. For color images this is 3.",
+    )
+    parser.add_argument("--num_epochs", type=int, default=5, help="number of epochs of training")
+    parser.add_argument("--output_dir", type=Path, default=Path("./output"), help="Name of the directory to dump generated images during training.")
+    parser.add_argument(
+        "--push_to_hub",
+        action="store_true",
+        help="Whether to push the model to the HuggingFace hub after training.",
+        )
+    parser.add_argument(
+        "--model_name",
+        default=None,
+        type=str,
+        help="Name of the model on the hub.",
+    )
+    parser.add_argument(
+        "--organization_name",
+        default="huggan",
+        type=str,
+        help="Organization name to push to, in case args.push_to_hub is specified.",
+    )
+    args = parser.parse_args()
+    
+    if args.push_to_hub:
+        assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed."
+        assert args.model_name is not None, "Need a `model_name` to create a repo when `--push_to_hub` is passed."
+
+    if args.output_dir is not None:
+        os.makedirs(args.output_dir, exist_ok=True)
+    
+    return args
+
+
+def stack_generator_layers(model, units):
+    model.add(layers.Conv2DTranspose(units, (4, 4), strides=2, padding='same', use_bias=False))
+    model.add(layers.BatchNormalization())
+    model.add(layers.LeakyReLU())  
+    return model 
+
+
+def create_generator(channel, hidden_size, latent_dim):
+    generator = tf.keras.Sequential()
+    generator.add(layers.Input((latent_dim,))) # 
+    generator.add(layers.Dense(hidden_size*4*7*7, use_bias=False, input_shape=(100,)))
+    generator.add(layers.LeakyReLU())
+
+    generator.add(layers.Reshape((7, 7, hidden_size*4)))
+
+    units = [hidden_size*2, hidden_size*1]
+    for unit in units:
+        generator = stack_generator_layers(generator, unit)
+
+    generator.add(layers.Conv2DTranspose(args.num_channels, (4, 4), strides=1, padding='same', use_bias=False, activation='tanh'))
+    return generator
+
+
+def stack_discriminator_layers(model, units, use_batch_norm=False, use_dropout=False):
+    model.add(layers.Conv2D(units, (4, 4), strides=(2, 2), padding='same'))
+    if use_batch_norm:
+        model.add(layers.BatchNormalization())
+    if use_dropout:
+        model.add(layers.Dropout(0.1))
+    model.add(layers.LeakyReLU())
+    return model
+
+
+def create_discriminator(channel, hidden_size, args):
+    discriminator = tf.keras.Sequential()
+    discriminator.add(layers.Input((args.image_size, args.image_size, args.num_channels)))
+    discriminator = stack_discriminator_layers(discriminator, hidden_size, use_batch_norm = True,  use_dropout = True)
+    discriminator = stack_discriminator_layers(discriminator, hidden_size * 2)
+    discriminator = stack_discriminator_layers(discriminator,True, hidden_size*4)
+    discriminator = stack_discriminator_layers(discriminator,True, hidden_size*16)
+
+    discriminator.add(layers.Flatten())
+    discriminator.add(layers.Dense(1))
+
+    return discriminator
+
+
+def discriminator_loss(real_image, generated_image):
+    real_loss = cross_entropy(tf.ones_like(real_image), real_image)
+    fake_loss = cross_entropy(tf.zeros_like(generated_image), generated_image)
+    total_loss = real_loss + fake_loss
+    return total_loss
+
+
+@tf.function
+def train_step(images):
+    noise = tf.random.normal([128, 100])
+
+    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
+      generated_images = generator(noise, training=True)
+
+      real_image = discriminator(images, training=True)
+      generated_image = discriminator(generated_images, training=True)
+      # calculate loss inside train step
+      gen_loss = cross_entropy(tf.ones_like(generated_image), generated_image)
+      disc_loss = discriminator_loss(real_image, generated_image)
+
+    gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)
+    gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)
+
+    generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))
+    discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))
+
+
+def generate_and_save_images(model, epoch, test_input, output_dir, number_of_examples_to_generate):
+  
+  predictions = model(test_input, training=False)
+
+  fig = plt.figure(figsize=(number_of_examples_to_generate*4, number_of_examples_to_generate*16))
+
+  for i in range(predictions.shape[0]):
+      plt.subplot(1, number_of_examples_to_generate, i+1)
+      if args.num_channels == 1:
+        plt.imshow(predictions[i, :, :, :], cmap='gray')
+      else:
+        plt.imshow(predictions[i, :, :, :])
+          
+      plt.axis('off')
+
+  plt.savefig(f'{output_dir}/image_at_epoch_{epoch}.png')
+
+
+def train(dataset, epochs, output_dir, args):
+  for epoch in range(epochs):
+    print("Epoch:", epoch)
+    for image_batch in tqdm(dataset):
+      train_step(image_batch)
+
+    generate_and_save_images(generator,
+                             epoch + 1,
+                             seed,
+                             output_dir,
+                             args.number_of_examples_to_generate)
+
+
+def preprocess(examples):
+    images = (np.asarray(examples["image"]).astype('float32')- 127.5) / 127.5
+    images = np.expand_dims(images, -1)
+    examples["pixel_values"] = images
+    return examples
+
+
+def preprocess_images(dataset, args):
+    data_collator = DefaultDataCollator(return_tensors="tf")
+    processed_dataset = dataset.map(preprocess)
+    
+    tf_train_dataset = processed_dataset["train"].to_tf_dataset(
+	    columns=['pixel_values'],
+	    shuffle=True,
+	    batch_size=args.batch_size,
+	    collate_fn=data_collator)
+
+    return tf_train_dataset
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    print("Downloading dataset..")
+    dataset = load_dataset(args.dataset)
+    dataset= preprocess_images(dataset, args)
+    print("Training model..")
+    generator = create_generator(args.num_channels, args.generator_hidden_size, args.latent_dim)
+    discriminator = create_discriminator(args.num_channels, args.discriminator_hidden_size, args)
+    generator_optimizer = tf.keras.optimizers.Adam(1e-4)
+    discriminator_optimizer = tf.keras.optimizers.Adam(1e-4)
+
+    # create seed with dimensions of number of examples to generate and noise
+    seed = tf.random.normal([args.number_of_examples_to_generate, args.latent_dim])
+
+    cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)
+
+    train(dataset, args.num_epochs, args.output_dir, args)
+    if args.push_to_hub is not None:
+
+        push_to_hub_keras(generator, repo_path_or_name=f"{args.output_dir}/{args.model_name}",organization=args.organization_name)
diff --git a/huggan/utils/README.md b/huggan/utils/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..3f05a7327f37d81ba18ff4042208355921816aeb
--- /dev/null
+++ b/huggan/utils/README.md
@@ -0,0 +1,51 @@
+# 🤗 Upload custom image dataset to the hub
+
+This directory contains an example script that showcases how to upload a custom image dataset to the hub programmatically (using Python).
+
+In this example, we'll upload all available datasets shared by the [CycleGAN authors](https://github.com/junyanz/CycleGAN/blob/master/datasets/download_dataset.sh) to the hub.
+
+It leverages the [ImageFolder](https://huggingface.co/docs/datasets/v2.0.0/en/image_process#imagefolder) and `push_to_hub`
+functionalities of the 🤗 [Datasets](https://huggingface.co/docs/datasets/index) library. 
+
+It can be run as follows:
+
+### 1. Make sure to have git-LFS installed on your system:
+First, verify that you have git-LFS installed. This can be done by running:
+
+```bash
+git-lfs -v
+```
+
+If you get "command not found", then install it as follows:
+
+```bash
+sudo apt-get install git-lfs
+```
+
+### 2. Login with your HuggingFace account:
+Next, one needs to provide a token for authentication with the hub. This can be done by either running:
+ 
+```bash
+huggingface-cli login
+```
+
+or 
+
+```python
+from huggingface_hub import notebook_login
+
+notebook_login()
+```
+
+in case you're running in a notebook.
+
+### 3. Upload!
+Finally, uploading is as easy as:
+
+```bash
+python push_to_hub_example.py --dataset horse2zebra
+````
+
+The result can be seen [here](https://huggingface.co/datasets/huggan/horse2zebra).
+
+Note that it's not required to programmatically upload a dataset to the hub: you can also do it in your browser as explained in [this guide](https://huggingface.co/docs/datasets/upload_dataset).
diff --git a/huggan/utils/__init__.py b/huggan/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/huggan/utils/hub.py b/huggan/utils/hub.py
new file mode 100644
index 0000000000000000000000000000000000000000..53d4dcd6c08cfeb652fcda8f7dd553b2d08bc192
--- /dev/null
+++ b/huggan/utils/hub.py
@@ -0,0 +1,12 @@
+from typing import Optional
+
+from huggingface_hub import HfFolder, whoami
+
+def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
+    if token is None:
+        token = HfFolder.get_token()
+    if organization is None:
+        username = whoami(token)["name"]
+        return f"{username}/{model_id}"
+    else:
+        return f"{organization}/{model_id}"
diff --git a/huggan/utils/push_to_hub_example.py b/huggan/utils/push_to_hub_example.py
new file mode 100644
index 0000000000000000000000000000000000000000..af75e1a250c863a9dae3e7a7629d3c2f600902b9
--- /dev/null
+++ b/huggan/utils/push_to_hub_example.py
@@ -0,0 +1,27 @@
+import argparse
+from datasets import load_dataset
+from tqdm import tqdm
+
+# choose a dataset
+available_datasets = ["apple2orange", "summer2winter_yosemite", "horse2zebra", "monet2photo", "cezanne2photo", "ukiyoe2photo", "vangogh2photo", "maps", "cityscapes", "facades", "iphone2dslr_flower", "ae_photos", "grumpifycat"]
+
+def upload_dataset(dataset_name):
+    if dataset_name not in available_datasets:
+        raise ValueError("Please choose one of the supported datasets:", available_datasets)
+    
+    # step 1: load dataset
+    dataset = load_dataset("imagefolder", data_files=f"https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/{dataset_name}.zip")
+
+    # step 2: push to hub
+    dataset.push_to_hub(f"huggan/{dataset_name}")
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--dataset", default="apple2orange", type=str, help="Dataset to upload")
+    args = parser.parse_args()
+    
+    upload_dataset(args.dataset)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/jax-controlnet-sprint/README.md b/jax-controlnet-sprint/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..cce7ca01fc7957dc30c55588dcc43fc5096e0ab6
--- /dev/null
+++ b/jax-controlnet-sprint/README.md
@@ -0,0 +1,714 @@
+# JAX/Diffusers community sprint 🧨
+
+Welcome to the JAX/Diffusers community sprint! The goal of this sprint is to work on fun and creative diffusion models using JAX and Diffusers.
+
+In this event, we will create various applications with diffusion models in JAX/Flax and Diffusers using free TPU hours generously provided by Google Cloud.
+
+This document is a walkthrough on all the important information to make a submission to the JAX/Diffusers community sprint.
+
+Don't forget to fill out the [signup form]! 
+
+> 💡 Note: This document is still WIP and it only contains initial details of the event. We will keep updating this document as we make other relevant information available throughout the community sprint.
+
+## Table of Contents
+
+- [Organization](#organization)
+- [Important dates](#important-dates)
+- [Communication](#communication)
+- [Talks](#talks)
+- [Data and Pre-processing](#data-and-pre-processing)
+    - [Prepare a large local dataset](#prepare-a-large-local-dataset)
+    - [Prepare a dataset with MediaPipe and Hugging Face](#prepare-a-dataset-with-mediapipe-and-hugging-face)    
+- [Training ControlNet](#training-controlnet)
+    - [Setting up your TPU VM](#setting-up-your-tpu-vm)
+    - [Installing JAX](#installing-jax)
+    - [Running the training script](#running-the-training-script)
+    - [TroubleShoot your TPU VM](#troubleshoot-your-tpu-vm)
+- [How to Make a Submission](#how-to-make-a-submission)
+    - [Pushing model weights and the model card to Hub](#pushing-model-weights-and-the-model-card-to-hub)
+- [Creating our Space](#creating-our-space)
+- [Prizes](#prizes)
+- [Jury](#jury)
+- [FAQ](#faq)
+    - [How to Use VSCode with TPU VM?](#how-to-use-vscode-with-tpu-vm)
+    - [How to Test Your Code Locally?](#how-to-test-your-code-locally)
+- [Sprint winners](#sprint-winners)
+
+## Organization 
+
+Participants can propose ideas for an interesting project involving diffusion models. Teams of 3 to 5 will then be formed around the most promising and interesting projects. Make sure to read through the [Communication](#communication) section on how to propose projects, comment on other participants' project ideas, and create a team.
+
+To help each team successfully finish their project, we will organize talks by leading scientists and engineers from Google, Hugging Face, and the open-source diffusion community. The talks will take place on 17th of April. Make sure to attend the talks to get the most out of your participation! Check out the [Talks](#talks) section to get an overview of the talks, including the speaker and the time of the talk.
+
+Each team is then given **free access to a TPU v4-8 VM** from April 14 to May 1st. In addition, we will provide a training example in JAX/Flax and Diffusers to train [ControlNets](https://huggingface.co/blog/controlnet) to kick-start your project. We will also provide examples of how to prepare datasets for ControlNet training. During the sprint, we'll make sure to answer any questions you might have about JAX/Flax and Diffusers and help each team as much as possible to complete their projects!
+
+> 💡 Note: We will not be distributing TPUs for single member teams, so you are encouraged to either join a team or find teammates for your idea. 
+
+At the end of the community sprint, each submission will be evaluated by a jury and the top-3 demos will be awarded a prize. Check out the [How to submit a demo] (TODO) section for more information and suggestions on how to submit your project.
+
+> 💡 Note: Even though we provide an example for performing ControlNet training, participants can propose ideas that do not involve ControlNets at all. But the ideas need to be centered around diffusion models.
+
+## Important dates
+
+- **29.03.** Official announcement of the community week. Make sure to fill out the [signup form].
+- **31.03.** Start forming groups in #jax-diffusers-ideas channel in Discord. 
+- **10.04.** Data collection.
+- **13.04. - 14.04. - [17.04.](https://www.youtube.com/watch?v=SOj2sxgvFe0)** Kick-off event with talks on Youtube. 
+- **14.04. - 17.04.** Start providing access to TPUs. 
+- **01.05.** Shutdown access to TPUs. 
+- **08.05.**: Announcement of the top 10 projects and prizes.
+
+> 💡 Note: We will be accepting applications throughout the sprint.
+
+## Communication
+
+All important communication will take place on our Discord server. Join the server using [this link](https://hf.co/join/discord). After you join the server, take the Diffusers role in `#role-assignment` channel and head to `#jax-diffusers-ideas` channel to share your idea as a forum post. To sign up for participation, fill out the [signup form] and we will give you access to two more Discord channels on discussions and technical support, and access to TPUs.
+Important announcements of the Hugging Face, Flax/JAX, and Google Cloud team will be posted in the server.
+
+The Discord server will be the central place for participants to post about their results, share their learning experiences, ask questions and get technical support in various obstacles they encounter.
+
+For issues with Flax/JAX, Diffusers, Datasets or for questions that are specific to your project we will be interacting through public repositories and forums:
+
+- Flax: [Issues](https://github.com/google/flax/issues), [Questions](https://github.com/google/flax/discussions)
+- JAX: [Issues](https://github.com/google/jax/issues), [Questions](https://github.com/google/jax/discussions)
+- 🤗 Diffusers: [Issues](https://github.com/huggingface/diffusers/issues), [Questions](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63)
+- 🤗 Datasets: [Issues](https://github.com/huggingface/datasets/issues), [Questions](https://discuss.huggingface.co/c/datasets/10)
+- Project specific questions: Can be asked from each project's own post on #jax-diffusers-ideas channel on Discord.
+- TPU related questions: `#jax-diffusers-tpu-support` channel on Discord. 
+- General discussion: `#jax-diffusers-sprint channel` on Discord.
+You will get access to `#jax-diffusers-tpu-support` and `#jax-diffusers-sprint` once you are accepted to attend the sprint.
+
+When asking for help, we encourage you to post the link to [forum](https://discuss.huggingface.co) post to the Discord server, instead of directly posting issues or questions. 
+This way, we make sure that the everybody in the community can benefit from your questions, even after the community sprint.
+
+> 💡 Note: After 10th of April, if you have signed up on the google form, but you are not in the Discord channel, please leave a message on [the official forum announcement](https://discuss.huggingface.co/t/controlling-stable-diffusion-with-jax-and-diffusers-using-v4-tpus/35187/2) and ping `@mervenoyan`, `@sayakpaul`, and `@patrickvonplaten`. We might take a day to process these requests.
+
+## Talks
+
+We have invited prominent researchers and engineers from Google, Hugging Face, and the open-source community who are working in the Generative AI space. We will update this section with links to the talks, so keep an eye here or on Discord in diffusion models core-announcements channel and set your reminders!
+
+### **April 13, 2023**
+
+| Speaker	| Topic	| Time	| Video |
+|---|---|---|---|
+[Emiel Hoogeboom, Google Brain](https://twitter.com/emiel_hoogeboom?lang=en)	| Pixel-Space Diffusion models for High Resolution Images | 4.00pm-4.40pm CEST / 7.00am-7.40am PST| [![Youtube](https://www.youtube.com/s/desktop/f506bd45/img/favicon_32.png)](https://www.youtube.com/watch?v=iw2WCAGxdQ4) |
+| [Apolinário Passos, Hugging Face](https://twitter.com/multimodalart?lang=en)	| Introduction to Diffusers library	|  4.40pm-5.20pm CEST / 7.40am-08.20am PST	| [![Youtube](https://www.youtube.com/s/desktop/f506bd45/img/favicon_32.png)](https://www.youtube.com/watch?v=iw2WCAGxdQ4)
+| [Ting Chen, Google Brain](https://twitter.com/tingchenai?lang=en)	| Diffusion++: discrete data and high-dimensional generation |	 5.45pm-6.25pm CEST / 08.45am-09.25am PST	| [![Youtube](https://www.youtube.com/s/desktop/f506bd45/img/favicon_32.png)](https://www.youtube.com/watch?v=iw2WCAGxdQ4) |
+### **April 14, 2023**
+
+| Speaker	| Topic	| Time	| Video |
+|---|---|---|---|
+| [Tim Salimans, Google Brain](https://twitter.com/timsalimans?lang=en)	| Efficient image and video generation with distilled diffusion models |   4.00pm-4.40pm CEST / 7.00am-7.40am PST| [![Youtube](https://www.youtube.com/s/desktop/f506bd45/img/favicon_32.png)](https://www.youtube.com/watch?v=6f5chgbKjSg&ab_channel=HuggingFace) |
+| [Suraj Patil, Hugging Face](https://twitter.com/psuraj28?lang=en)	| Masked Generative Models: MaskGIT/Muse	|  4.40pm-5.20pm CEST / 7.40am-08.20am PST	| [![Youtube](https://www.youtube.com/s/desktop/f506bd45/img/favicon_32.png)](https://www.youtube.com/watch?v=6f5chgbKjSg&ab_channel=HuggingFace) |
+| [Sabrina Mielke, John Hopkins University](https://twitter.com/sjmielke?lang=en)	| From stateful code to purified JAX: how to build your neural net framework |	 5.20pm-6.00pm CEST / 08.20am-09.00am PST	| [![Youtube](https://www.youtube.com/s/desktop/f506bd45/img/favicon_32.png)](https://www.youtube.com/watch?v=6f5chgbKjSg&ab_channel=HuggingFace) |
+
+### **April 17, 2023**
+
+| Speaker	| Topic	| Time	| Video |
+|---|---|---|---|
+| [Andreas Steiner, Google Brain](https://twitter.com/AndreasPSteiner)	| JAX & ControlNet |  4.00pm-4.40pm CEST / 7.00am-7.40am PST| [![Youtube](https://www.youtube.com/s/desktop/f506bd45/img/favicon_32.png)](https://www.youtube.com/watch?v=SOj2sxgvFe0) |
+| [Boris Dayma, craiyon](https://twitter.com/borisdayma?lang=en)	| DALL-E Mini	|  4.40pm-5.20pm CEST / 7.40am-08.20am PST	| [![Youtube](https://www.youtube.com/s/desktop/f506bd45/img/favicon_32.png)](https://www.youtube.com/watch?v=SOj2sxgvFe0) |
+| [Margaret Mitchell, Hugging Face](https://twitter.com/mmitchell_ai?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor)	| Ethics of Text-to-Image |	 5.20pm-6.00pm CEST / 08.20am-09.00am PST	| [![Youtube](https://www.youtube.com/s/desktop/f506bd45/img/favicon_32.png)](https://www.youtube.com/watch?v=SOj2sxgvFe0) |
+
+[signup form]: https://forms.gle/t3M7aNPuLL9V1sfa9
+
+## Data and Pre-Processing
+
+In this section, we will cover how to build your own dataset for ControlNet training.
+
+### Prepare a large local dataset
+
+#### Mount a disk 
+
+If you need extra space, you can follow [this guide](https://cloud.google.com/tpu/docs/setup-persistent-disk#prerequisites) to create a persistent disk, attach it to your TPU VM, and create a directory to mount the disk. You can then use this directory to store your dataset.
+
+As a side note, the TPU VM allocated to your team has a 3 TB persistent storage drive attached to it. To learn how to use it, check out [this guide](https://cloud.google.com/tpu/docs/setup-persistent-disk#mount-pd). 
+
+#### Data preprocessing 
+
+Here we demonstrate how to prepare a large dataset to train a ControlNet model with canny edge detection. More specifically, we provide an [example script](./dataset_tools/coyo_1m_dataset_preprocess.py) that:
+* Selects 1 million image-text pairs from an existing dataset [COYO-700M](https://huggingface.co/datasets/kakaobrain/coyo-700m).
+* Downloads each image and use Canny edge detector to generate the conditioning image. 
+* Create a metafile that links all the images and processed images to their text captions. 
+
+Use the following command to run the example data preprocessing script. If you've mounted a disk to your TPU, you should place your `train_data_dir` and `cache_dir` on the mounted disk
+
+```bash
+python3 coyo_1m_dataset_preprocess.py \
+ --train_data_dir="/mnt/disks/persist/data" \
+ --cache_dir="/mnt/disks/persist" \
+ --max_train_samples=1000000 \
+ --num_proc=16
+```
+
+Once the script finishes running, you can find a data folder at the specified `train_data_dir` with the below folder structure:
+
+```
+data
+├── images
+│   ├── image_1.png
+│   ├── .......
+│   └── image_1000000.jpeg
+├── processed_images
+│   ├── image_1.png
+│   ├── .......
+│   └── image_1000000.jpeg
+└── meta.jsonl
+```
+
+#### Load dataset
+
+To load a dataset from the data folder you just created, you should add a dataset loading script to your data folder. The dataset loading script should have the same name as the folder. For example, if your data folder is `data`, you should add a data loading script named `data.py`. We provide an [example data loading script](./dataset_tools/data.py) for you to use. All you need to do is to update the `DATA_DIR` with the correct path to your data folder. For more details about how to write a dataset loading script, refer to the [documentation](https://huggingface.co/docs/datasets/dataset_script).
+
+Once the dataset loading script is added to your data folder, you can load it with: 
+
+```python
+dataset = load_dataset("/mnt/disks/persist/data", cache_dir="/mnt/disks/persist" )
+```
+
+Note that you can use the `--train_data_dir` flag to pass your data folder directory to the training script and generate your dataset automatically during the training.
+ 
+For large datasets, we recommend generating the dataset once and saving it on the disk with
+
+```python
+dataset.save_to_disk("/mnt/disks/persist/dataset")
+```
+
+You can then reuse the saved dataset for your training by passing the `--load_from_disk` flag.
+
+Here is an example to run a training script that will load the dataset from the disk 
+
+```python
+export MODEL_DIR="runwayml/stable-diffusion-v1-5"
+export OUTPUT_DIR="/mnt/disks/persist/canny_model"
+export DATASET_DIR="/mnt/disks/persist/dataset"
+export DISK_DIR="/mnt/disks/persist"
+
+python3 train_controlnet_flax.py \
+ --pretrained_model_name_or_path=$MODEL_DIR \
+ --output_dir=$OUTPUT_DIR \
+ --train_data_dir=$DATASET_DIR \
+ --load_from_disk \
+ --cache_dir=$DISK_DIR \
+ --resolution=512 \
+ --learning_rate=1e-5 \
+ --train_batch_size=2 \
+ --revision="non-ema" \
+ --from_pt \
+ --max_train_steps=500000 \
+ --checkpointing_steps=10000 \
+ --dataloader_num_workers=16 
+ ```
+
+### Prepare a dataset with MediaPipe and Hugging Face 
+
+We provide a notebook ([![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/community-events/blob/main/jax-controlnet-sprint/dataset_tools/create_pose_dataset.ipynb)) that shows you how to prepare a dataset for ControlNet training using [MediaPipe](https://developers.google.com/mediapipe) and Hugging Face. Specifically, in the notebook, we show:
+
+* How to leverage MediaPipe solutions to extract pose body joints from the input images.
+* Predict captions using BLIP-2 from the input images using 🤗 Transformers.
+* Build and push the final dataset to the Hugging Face Hub using 🤗 Datasets. 
+
+You can refer to the notebook to create your own datasets using other MediaPipe solutions as well. Below, we list all the relevant ones:
+
+* [Pose Landmark Detection](https://developers.google.com/mediapipe/solutions/vision/pose_landmarker)
+* [Face Landmark Detection](https://developers.google.com/mediapipe/solutions/vision/face_landmarker)
+* [Selfie Segmentation](https://developers.google.com/mediapipe/solutions/vision/image_segmenter)
+
+
+## Training ControlNet
+
+This is perhaps the most fun and interesting part of this document as here we show you how to train a custom ControlNet model. 
+
+> 💡 Note: For this sprint, you are NOT restricted to just training ControlNets. We provide this training script as a reference for you to get started. 
+
+For faster training on TPUs and GPUs you can leverage the Flax training example. Follow the instructions above to get the model and dataset before running the script.
+
+### Setting up your TPU VM
+
+_Before proceeding with the rest of this section, you must ensure that the
+email address you're using has been added to the `hf-flax` project on
+Google Cloud Platform. If it's not the case, please let us know in the Discord server (you can tag `@sayakpaul`, `@merve`, and `@patrickvonplaten`)._
+
+In the following, we will describe how to do so using a standard console, but you should also be able to connect to the TPU VM via IDEs, like Visual Studio Code, etc.
+
+1. You need to install the [Google Cloud SDK](https://cloud.google.com/sdk/docs/install). Please follow the instructions on https://cloud.google.com/sdk.
+
+2. Once you've installed the Google Cloud SDK, you should set your account by running the following command. Make sure that <your-email-address> corresponds to the gmail address you used to sign up for this event.
+  
+    ```bash
+    gcloud config set account <your-email-adress>
+    ```
+
+3. Let's also make sure the correct project is set in case your email is used for multiple gcloud projects:
+
+    ```bash
+    gcloud config set project hf-flax
+    ```
+
+4. Next, you will need to authenticate yourself. You can do so by running:
+
+    ```bash
+    gcloud auth login
+    ```
+
+    This should give you a link to a website, where you can authenticate your gmail account.
+
+5. Finally, you can establish an SSH tunnel into the TPU VM! Please run the following command by setting`--zone` to `us-central2-b` and to the TPU name also sent to you via email from the Hugging Face team.
+
+    ```bash
+    gcloud alpha compute tpus tpu-vm ssh <tpu-name> --zone <zone> --project hf-flax
+    ```
+
+This should establish an SSH tunnel into the TPU VM!
+    
+> 💡 Note: You are NOT supposed to have access to the Google Cloud console. Also, you might not get an invitation link to join the `hf-flax` project. But you should still be able to access the TPU VM following the above steps.     
+
+> 💡 Note: The TPU VMs are already attached to persistent storage drives (of 3 TB). This will be helpful
+in case your team wants to perform training on a large dataset locally. The disk name of the storage drive should also be present in the email you received. Follow [this section](https://github.com/huggingface/community-events/tree/main/jax-controlnet-sprint#mount-a-disk) for more details.
+
+### Installing JAX
+
+Let's first create a Python virtual environment:
+
+```bash
+python3 -m venv <your-venv-name>
+```
+
+We can activate the environment by running:
+
+```bash
+source ~/<your-venv-name>/bin/activate
+```
+
+Then install Diffusers and the library's training dependencies:
+
+```bash
+pip install git+https://github.com/huggingface/diffusers.git
+```
+
+Then clone this repository and install JAX, Flax and the other dependencies:
+
+```bash
+git clone https://github.com/huggingface/community-events
+cd community-events/jax-controlnet-sprint/training_scripts
+pip install -U -r requirements_flax.txt
+```
+
+To verify that JAX was correctly installed, you can run the following command:
+
+```python
+import jax
+jax.device_count()
+```
+
+This should display the number of TPU cores, which should be 4 on a TPUv4-8 VM. If Python is not able to detect the TPU device, please take a look at [this section](#troubleshoot-your-tpu-vm) for solutions.
+
+If you want to use Weights and Biases logging, you should also install `wandb` now:
+
+```bash
+pip install wandb
+```
+
+> 💡 Note: Weights & Biases is free for students, educators, and academic researchers. All participants of our event are qualified to get an academic Weights & Biases team account. To create your team, you can visit https://wandb.ai/create-team and choose the team type to be "Academic". For more information regarding creation and management of Weights & Biases team, you can checkout https://docs.wandb.ai/guides/app/features/teams.
+### Running the training script
+
+Now let's download two conditioning images that we will use to run validation during the training in order to track our progress
+
+```bash
+wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_1.png
+wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_2.png
+```
+
+We encourage you to store or share your model with the community. To use Hugging Face hub, please login to your Hugging Face account, or ([create one](https://huggingface.co/docs/diffusers/main/en/training/hf.co/join) if you don’t have one already):
+
+```bash
+huggingface-cli login
+```
+
+Make sure you have the `MODEL_DIR`,`OUTPUT_DIR` and `HUB_MODEL_ID` environment variables set. The `OUTPUT_DIR` and `HUB_MODEL_ID` variables specify where to save the model to on the Hub:
+
+```bash
+export MODEL_DIR="runwayml/stable-diffusion-v1-5"
+export OUTPUT_DIR="runs/fill-circle-{timestamp}"
+export HUB_MODEL_ID="controlnet-fill-circle"
+```
+
+And finally start the training (make sure you're in the `jax-controlnet-sprint/training_scripts` directory)!
+
+```bash
+python3 train_controlnet_flax.py \
+ --pretrained_model_name_or_path=$MODEL_DIR \
+ --output_dir=$OUTPUT_DIR \
+ --dataset_name=fusing/fill50k \
+ --resolution=512 \
+ --learning_rate=1e-5 \
+ --validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \
+ --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \
+ --validation_steps=1000 \
+ --train_batch_size=2 \
+ --revision="non-ema" \
+ --from_pt \
+ --report_to="wandb" \
+ --tracker_project_name=$HUB_MODEL_ID \
+ --num_train_epochs=11 \
+ --push_to_hub \
+ --hub_model_id=$HUB_MODEL_ID
+ ```
+
+Note that `--from_pt` argument will convert your pytorch checkpoint into flax. However, it will only work with checkpoints in diffusers format. If your `MODEL_DIR` does not contain checkpoints in diffusers format, you cannot use the `--from_pt` argument. You can convert your `ckpt` or `safetensors` checkpoints into diffusers format using [this script](https://github.com/huggingface/diffusers/blob/main/scripts/convert_original_stable_diffusion_to_diffusers.py). 
+
+Since we passed the `--push_to_hub` flag, it will automatically create a model repo under your Hugging Face account based on `$HUB_MODEL_ID`. By the end of training, the final checkpoint will be automatically stored on the hub. You can find an example model repo [here](https://huggingface.co/YiYiXu/fill-circle-controlnet).
+
+Our training script also provides limited support for streaming large datasets from the Hugging Face Hub. In order to enable streaming, one must also set `--max_train_samples`.  Here is an example command (from [this blog article](https://huggingface.co/blog/train-your-controlnet)):
+
+```bash
+export MODEL_DIR="runwayml/stable-diffusion-v1-5"
+export OUTPUT_DIR="runs/uncanny-faces-{timestamp}"
+export HUB_MODEL_ID="controlnet-uncanny-faces"
+
+python3 train_controlnet_flax.py \
+ --pretrained_model_name_or_path=$MODEL_DIR \
+ --output_dir=$OUTPUT_DIR \
+ --dataset_name=multimodalart/facesyntheticsspigacaptioned \
+ --streaming \
+ --conditioning_image_column=spiga_seg \
+ --image_column=image \
+ --caption_column=image_caption \
+ --resolution=512 \
+ --max_train_samples 100000 \
+ --learning_rate=1e-5 \
+ --train_batch_size=1 \
+ --revision="flax" \
+ --report_to="wandb" \
+ --tracker_project_name=$HUB_MODEL_ID
+```
+
+Note, however, that the performance of the TPUs might get bottlenecked as streaming with `datasets` is not optimized for images. For ensuring maximum throughput, we encourage you to explore the following options:
+
+* [Webdataset](https://webdataset.github.io/webdataset/)
+* [TorchData](https://github.com/pytorch/data)
+* [TensorFlow Datasets](https://www.tensorflow.org/datasets/tfless_tfds)
+
+
+When work with a larger dataset, you may need to run training process for a long time and it’s useful to save regular checkpoints during the process. You can use the following argument to enable intermediate checkpointing:
+
+```bash
+ --checkpointing_steps=500
+```
+This will save the trained model in subfolders of your output_dir. Subfolder names is the number of steps performed so far; for example: a checkpoint saved after 500 training steps would be saved in a subfolder named 500 
+
+You can then start your training from this saved checkpoint with 
+
+```bash
+ --controlnet_model_name_or_path="./control_out/500" 
+```
+
+We support training with the Min-SNR weighting strategy proposed in [Efficient Diffusion Training via Min-SNR Weighting Strategy](https://arxiv.org/abs/2303.09556) which helps to achieve faster convergence by rebalancing the loss. To use it, one needs to set the `--snr_gamma` argument. The recommended value when using it is `5.0`.
+
+We also support gradient accumulation - it is a technique that lets you use a bigger batch size than your machine would normally be able to fit into memory. You can use `gradient_accumulation_steps` argument to set gradient accumulation steps. The ControlNet author recommends using gradient accumulation to achieve better convergence. Read more [here](https://github.com/lllyasviel/ControlNet/blob/main/docs/train.md#more-consideration-sudden-converge-phenomenon-and-gradient-accumulation).
+
+You can **profile your code** with:
+
+```bash
+ --profile_steps==5
+```
+
+Refer to the [JAX documentation on profiling](https://jax.readthedocs.io/en/latest/profiling.html). To inspect the profile trace, you'll have to install and start Tensorboard with the profile plugin:
+
+```bash
+pip install tensorflow tensorboard-plugin-profile
+tensorboard --logdir runs/fill-circle-100steps-20230411_165612/
+```
+
+
+The profile can then be inspected at http://localhost:6006/#profile
+
+Sometimes you'll get version conflicts (error messages like `Duplicate plugins for name projector`), which means that you have to uninstall and reinstall all versions of Tensorflow/Tensorboard (e.g. with `pip uninstall tensorflow tf-nightly tensorboard tb-nightly tensorboard-plugin-profile && pip install tf-nightly tbp-nightly tensorboard-plugin-profile`).
+
+Note that the debugging functionality of the Tensorboard `profile` plugin is still under active development. Not all views are fully functional, and for example the `trace_viewer` cuts off events after 1M (which can result in all your device traces getting lost if you for example profile the compilation step by accident).
+
+### Troubleshoot your TPU VM
+
+**VERY IMPORTANT** - Only one process can access the TPU cores at a time. This means that if multiple team members are trying to connect to the TPU cores, you will get errors such as:
+
+```
+libtpu.so already in used by another process. Not attempting to load libtpu.so in this process.
+```
+
+We recommend every team member create her/his own virtual environment, but only one person should run the heavy training processes. Also, please take turns when setting up the TPUv4-8 so that everybody can verify that JAX is correctly installed.
+
+If your team members are not currently using the TPU but you still get this error message. You should kill the process that is using the TPU with 
+
+```
+kill -9 PID
+```
+
+you will need to replace the term “PID” with the PID of the process that uses TPU. In most cases, this information is included in the error message. For example, if you get 
+
+```
+The TPU is already in use by a process with pid 1378725. Not attempting to load libtpu.so in this process.
+```
+
+you can do
+
+```
+kill -9 1378725
+```
+
+You can also use the below command to find processes using each of the TPU chips (e.g. `/dev/accel0` is one of the TPU chips)
+
+```
+sudo lsof -w /dev/accel0
+```
+
+To kill all the processes using `/dev/accel0` 
+
+```
+sudo lsof -t /dev/accel0 | xargs kill -9
+```
+
+If Python is not able to detect your TPU device (i.e. when you do `jax.device_count()` and it outputs `0`), it might be because you have no rights to access the tpu logs, or you have a dangling tpu lock file. Run these commands usually fix the issue
+
+```
+sudo rm -f /tmp/libtpu_lockfile
+```
+
+```
+sudo chmod o+w /tmp/tpu_logs/
+```
+
+<div id="how-to-make-a-submission">
+<h2> How to Make a Submission </h2>
+</div>
+
+To make a full submission, you need to have the following on Hugging Face Hub:
+- Model repository with model weights and model card,
+- (Optional) Dataset repository with dataset card, 
+- A Hugging Face Space that lets others interact with your model.
+
+### Pushing model weights and the model card to Hub
+
+**If you are using the training script (`train_controlnet_flax.py`) provided in this directory**
+
+Enabling `push_to_hub` argument in the training arguments will:
+- Create a model repository locally, and remotely on Hugging Face Hub,
+- Create a model card and write it to the local model repository,
+- Save your model to the local model repository,
+- Push the local repository to Hugging Face Hub.
+
+Your automatically generated model card will look like below 👇 
+![Model Card](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/jax_model_card.png).
+
+You can edit the model card to be more informative. Model cards that are more informative than the others will carry more weight during evaluation.
+
+**If you have trained a custom model and not used the script**
+
+You need to authenticate yourself with `huggingface-cli login` as instructed above. If you are using one of the available model classes from `diffusers`, save your model with `save_pretrained` method of your model. 
+
+```python
+model.save_pretrained("path_to_your_model_repository")
+```
+
+After saving your model to a folder, you can simply use below script to push your model to the Hub 👇 
+
+```python
+from huggingface_hub import create_repo, upload_folder
+
+create_repo("username/my-awesome-model")
+upload_folder(
+    folder_path="path_to_your_model_repository",
+    repo_id="username/my-awesome-model"
+)
+```
+
+This will push your model to Hub. After pushing your model to Hub, you need to create the model card yourself. 
+You can use graphical interface to edit the model card. 
+![Edit Model Card](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/edit_model_card.png)
+
+Every model card consists of two sections, metadata and free text. You can edit metadata from the sections in graphical UI. If you have saved your model using `save_pretrained`, you do not need to provide `pipeline_tag` and `library_name`. If not, provide `pipeline_tag`, `library_name` and dataset if it exists on Hugging Face Hub. Aside from these, you need to add the `jax-diffusers-event` to `tags` section.
+```
+---
+license: apache-2.0
+library_name: diffusers
+tags:
+- jax-diffusers-event
+datasets:
+- red_caps
+pipeline_tag: text-to-image
+---
+```
+![Edit Metadata](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/edit_metadata.png)
+
+### Creating our Space
+
+<h4> Writing our Application </h4>
+
+    
+We will use [Gradio](https://gradio.app/) to build our applications. Gradio has two main APIs: `Interface` and `Blocks`. `Interface` is a high-level API that lets you create an interface with few lines of code, and `Blocks` is a lower-level API that gives you more flexibility over interfaces you can build. The code should be included in a file called `app.py`.
+
+Let's try to create a ControlNet app as an example. The `Interface` API simply works like below 👇 
+
+```python
+import gradio as gr
+
+# inference function takes prompt, negative prompt and image
+def infer(prompt, negative_prompt, image):
+    # implement your inference function here
+    return output_image
+
+# you need to pass inputs and outputs according to inference function
+gr.Interface(fn = infer, inputs = ["text", "text", "image"], outputs = "image").launch()
+```
+You can customize your interface by passing `title`, `description` and `examples` to the `Interface` function.
+
+```python
+title = "ControlNet on Canny Filter"
+description = "This is a demo on ControlNet based on canny filter."
+# you need to pass your examples according to your inputs
+# each inner list is one example, each element in the list corresponding to a component in the `inputs`.
+examples = [["a cat with cake texture", "low quality", "cat_image.png"]]
+gr.Interface(fn = infer, inputs = ["text", "text", "image"], outputs = "image",
+            title = title, description = description, examples = examples, theme='gradio/soft').launch()
+```
+Your interface will look like below 👇 
+![ControlNet](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio_controlnet.png)
+
+With Blocks, you can add markdown, tabs, components under columns and rows and more. Assume we have two ControlNets and we want to include them in one Space. We will have them under different tabs under one demo like below 👇 
+
+```python
+import gradio as gr
+
+def infer_segmentation(prompt, negative_prompt, image):
+    # your inference function for segmentation control 
+    return im
+
+def infer_canny(prompt, negative_prompt, image):
+    # your inference function for canny control 
+    return im
+
+with gr.Blocks(theme='gradio/soft') as demo:
+    gr.Markdown("## Stable Diffusion with Different Controls")
+    gr.Markdown("In this app, you can find different ControlNets with different filters. ")
+
+
+    with gr.Tab("ControlNet on Canny Filter "):
+        prompt_input_canny = gr.Textbox(label="Prompt")
+        negative_prompt_canny = gr.Textbox(label="Negative Prompt")
+        canny_input = gr.Image(label="Input Image")
+        canny_output = gr.Image(label="Output Image")
+        submit_btn = gr.Button(value = "Submit")
+        canny_inputs = [prompt_input_canny, negative_prompt_canny, canny_input]
+        submit_btn.click(fn=infer_canny, inputs=canny_inputs, outputs=[canny_output])
+        
+    with gr.Tab("ControlNet with Semantic Segmentation"):
+        prompt_input_seg = gr.Textbox(label="Prompt")
+        negative_prompt_seg = gr.Textbox(label="Negative Prompt")
+        seg_input = gr.Image(label="Image")
+        seg_output = gr.Image(label="Output Image")
+        submit_btn = gr.Button(value = "Submit")
+        seg_inputs = [prompt_input_seg, negative_prompt_seg, seg_input]
+        submit_btn.click(fn=infer_segmentation, inputs=seg_inputs, outputs=[seg_output])
+
+demo.launch()
+```
+
+Above demo will look like below 👇 
+![Gradio Blocks](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio_controlnet_blocks.png)
+
+
+#### Creating our Space
+After our application is written, we can create a Hugging Face Space to host our app. You can go to [huggingface.co](http://huggingface.co), click on your profile on top right and select “New Space”.
+
+![New Space](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/new_space.png)
+
+
+We can name our Space, pick a license and select Space SDK as “Gradio”. 
+
+![Space Configuration](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/space_config.png)
+
+After creating the Space, you can either use the instructions below to clone the repository locally, add your files and push, or use the graphical interface to create the files and write the code in the browser.
+
+![Spaces Landing](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/repository_landing.png)
+
+To upload your application file, pick “Add File” and drag and drop your file.
+
+![New Space Landing](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/add_file.png)
+
+Lastly, we have to create a file called `requirements.txt` and add requirements of our project. Make sure to install below versions of jax, diffusers and other dependencies like below. 
+
+```
+-f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
+jax[cuda11_cudnn805]
+jaxlib
+git+https://github.com/huggingface/diffusers@main
+opencv-python
+transformers
+flax
+```
+
+We will give you GPU grant so your application can run on GPU.
+
+We have a leaderboard hosted [here](https://huggingface.co/spaces/jax-diffusers-event/leaderboard) and we will be distributing prizes from this leaderboard. To make your Space show up on the leaderboard, simply edit `README.md` of your Space to have the tag `jax-diffusers-event` under tags like below 👇 
+```
+---
+title: Canny Coyo1m
+emoji: 💜 
+...
+tags:
+- jax-diffusers-event
+---
+```
+
+## Prizes
+
+For this sprint we will have many prizes. We will pick the first ten projects from [this leaderboard](https://huggingface.co/spaces/jax-diffusers-event/leaderboard), so you should tag your Space for the leaderboard to make your submission complete, as instructed in above section. The projects are ranked by likes, so we will amplify the visibility of all projects for people to cast their votes by leaving a like on the Space. We will pick the first ten projects from the ranking, and the jury will cast their votes to determine the first three places. These projects will be highlighted by both Google and Hugging Face. Elaborately made interfaces as well as projects with open-sourced codebases and models will likely increase the chance of winning prizes. 
+
+Prizes are as follows and are given to each team member 👇 
+    
+**First Place**: A voucher of $150 that you can spend at [Hugging Face Store](https://store.huggingface.co/), Hugging Face Hub PRO subscription for one year, Natural Language Processing with Transformers book
+    
+**Second Place**: A voucher of $125 that you can spend at [Hugging Face Store](https://store.huggingface.co/), Hugging Face Hub PRO subscription for one year
+    
+**Third Place**: A voucher of $100 that you can spend at [Hugging Face Store](https://store.huggingface.co/), Hugging Face Hub PRO subscription for one year
+    
+The first ten projects on the leaderboard (regardless of jury decision) will win a merch set exclusively made for this sprint by Hugging Face, and an separate JAX merch set from Google. 
+    
+## Jury
+    
+Our jury panel for this sprint included:
+    
+1. Robin Rombach, Stability AI
+2. Huiwen Chang, Google Research
+3. Jun-Yan Zhu, Carnegie Mellon University
+4. Merve Noyan, Hugging Face
+
+
+## FAQ 
+
+In this section, We are collecting answers to frequently asked questions from our discord channel. Contributions welcome!
+
+### How to Use VSCode with TPU VM?
+
+You can follow this [general guide](https://medium.com/@ivanzhd/vscode-sftp-connection-to-compute-engine-on-google-cloud-platform-gcloud-9312797d56eb) on how to use VSCode remote to connect to Google Cloud VMs. Once it's set up, you can develop on the TPU VM using VSCode.
+
+To get your external IP, use this command:
+```
+gcloud compute tpus tpu-vm describe <node_name> --zone=<zone>
+```
+
+It should be listed under 'accessConfig' -> 'externalIp'
+
+### How to Test Your Code Locally?
+
+Since team members are sharing the TPU VM, it might be practical to write and test your code locally on a CPU while your teammates are running the training process on the VM. To run local testing, it is important to set the `xla_force_host_platform_device_count` flag to `4`. Read more on the [documentation](https://jax.readthedocs.io/en/latest/jax-101/06-parallelism.html#aside-hosts-and-devices-in-jax).
+    
+## Sprint winners
+    
+Top 10 projects (based on the likes on their demo applications) are available on this [leaderboard](https://huggingface.co/spaces/jax-diffusers-event/leaderboard). We tooks this leaderboard to our [jury](#jury) to judge the top 10 projects based on several factors such as open-source model checkpoints, datasets, and codebases, completeness of the model and dataset cards, etc. As a result, following three projects emerged as the winners:
+    
+1. [ControlNet for Interior Design](https://huggingface.co/spaces/controlnet-interior-design/controlnet-seg)
+2. [ControlNet for Adjusting Brightness](https://huggingface.co/spaces/ioclab/brightness-controlnet)
+3. [Stable Diffusion with Hand Control](https://huggingface.co/spaces/vllab/controlnet-hands)
+
+    
+    
diff --git a/jax-controlnet-sprint/dataset_tools/coyo_1m_dataset_preprocess.py b/jax-controlnet-sprint/dataset_tools/coyo_1m_dataset_preprocess.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf60a4f05ab5620347a2dc020b7c1ab285218863
--- /dev/null
+++ b/jax-controlnet-sprint/dataset_tools/coyo_1m_dataset_preprocess.py
@@ -0,0 +1,131 @@
+import argparse
+import logging
+import random
+
+import cv2
+import jsonlines
+import numpy as np
+import requests
+from datasets import load_dataset
+from PIL import Image
+
+logger = logging.getLogger(__name__)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Example of a data preprocessing script."
+    )
+    parser.add_argument(
+        "--train_data_dir",
+        type=str,
+        required=True,
+        help="The directory to store the dataset",
+    )
+    parser.add_argument(
+        "--cache_dir",
+        type=str,
+        required=True,
+        help="The directory to store cache",
+    )
+    parser.add_argument(
+        "--max_train_samples",
+        type=int,
+        default=None,
+        help="number of examples in the dataset",
+    )
+    parser.add_argument(
+        "--num_proc",
+        type=int,
+        default=1,
+        help="number of processors to use in `dataset.map()`",
+    )
+
+    args = parser.parse_args()
+    return args
+
+
+# filter for `max_train_samples``
+def filter_function(example):
+    if example["clip_similarity_vitb32"] < 0.3:
+        return False
+    if example["watermark_score"] > 0.4:
+        return False
+    if example["aesthetic_score_laion_v2"] < 6.0:
+        return False
+    return True
+
+
+def filter_dataset(dataset, max_train_samples):
+    small_dataset = dataset.select(range(max_train_samples)).filter(filter_function)
+    return small_dataset
+
+
+if __name__ == "__main__":
+    args = parse_args()
+
+    # load coyo-700
+    dataset = load_dataset(
+        "kakaobrain/coyo-700m",
+        cache_dir=args.cache_dir,
+        split="train",
+    )
+
+    # estimation the % of images filtered
+    filter_ratio = len(filter_dataset(dataset, 20000)) / 20000
+
+    # esimate max_train_samples based on
+    #   (1) filter_ratio we calculuted with 20k examples
+    #   (2) assumption that only 80% of the URLs are still valid
+    max_train_samples = int(args.max_train_samples / filter_ratio / 0.8)
+
+    # filter dataset down to 1 million
+    small_dataset = filter_dataset(dataset, max_train_samples)
+
+    def preprocess_and_save(example):
+        image_url = example["url"]
+        try:
+            # download original image
+            image = Image.open(requests.get(image_url, stream=True, timeout=5).raw)
+            image_path = f"{args.train_data_dir}/images/{example['id']}.png"
+            image.save(image_path)
+
+            # generate and save canny image
+            processed_image = np.array(image)
+
+            # apply random threholds
+            #   note that this should normally be applied on the fly during training.
+            #   But that's fine when dealing with a larger dataset like here.
+            threholds = (
+                random.randint(0, 255),
+                random.randint(0, 255),
+            )
+            processed_image = cv2.Canny(processed_image, min(threholds), max(threholds))
+            processed_image = processed_image[:, :, None]
+            processed_image = np.concatenate(
+                [processed_image, processed_image, processed_image], axis=2
+            )
+            processed_image = Image.fromarray(processed_image)
+            processed_image_path = (
+                f"{args.train_data_dir}/processed_images/{example['id']}.png"
+            )
+            processed_image.save(processed_image_path)
+
+            # write to meta.jsonl
+            meta = {
+                "image": image_path,
+                "conditioning_image": processed_image_path,
+                "caption": example["text"],
+            }
+            with jsonlines.open(
+                f"{args.train_data_dir}/meta.jsonl", "a"
+            ) as writer:  # for writing
+                writer.write(meta)
+
+        except Exception as e:
+            logger.error(f"Failed to process image{image_url}: {str(e)}")
+
+    # preprocess -> image, processed image and meta.jsonl
+    small_dataset.map(preprocess_and_save, num_proc=args.num_proc)
+
+    print(f"created data folder at: {args.train_data_dir}")
diff --git a/jax-controlnet-sprint/dataset_tools/create_pose_dataset.ipynb b/jax-controlnet-sprint/dataset_tools/create_pose_dataset.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..ffefec1074adca24eda52f49cbafe2eaf6ff93c7
--- /dev/null
+++ b/jax-controlnet-sprint/dataset_tools/create_pose_dataset.ipynb
@@ -0,0 +1,3165 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "jZ7n0aUCsO2M"
+   },
+   "source": [
+    "# Dataset preparation for ControlNet training with MediaPipe and Hugging Face\n",
+    "\n",
+    "_By: MediaPipe team (Google), Diffusers team (Hugging Face)_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "PI5zdqLlj3wa"
+   },
+   "source": [
+    "This notebook provides an end-to-end example of preparing a dataset that is compatible with [ControlNet training](https://github.com/huggingface/diffusers/tree/main/examples/controlnet) using MediaPipe and Hugging Face. Specifically, in this notebook, we\n",
+    "\n",
+    "* Load the [`horses_or_humans` dataset](https://www.tensorflow.org/datasets/catalog/horses_or_humans) using TensorFlow Datasets.\n",
+    "* Prepare conditioning images from the original images of the dataset using [MediaPipe Pose Landmark Detection](https://developers.google.com/mediapipe/solutions/vision/pose_landmarker). \n",
+    "* Generate captions of the original images using BLIP-2 using [🤗 Transformers](https://huggingface.co/docs/transformers).\n",
+    "* Prepare the final dataset using [🤗 Datasets](https://huggingface.co/docs/datasets) and push it to the Hugging Face Hub. \n",
+    "\n",
+    "Let's get started!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zX1aP71GlRbz"
+   },
+   "source": [
+    "## Install libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "id": "Pj5hwQKLSACL"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install -q mediapipe datasets transformers accelerate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "VrgKQ9X9lTHk"
+   },
+   "source": [
+    "## Load the dataset\n",
+    "\n",
+    "After we load the `train` set of the dataset, we filter it to only have the `humans` labels. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "id": "6_6M5AALVQYh"
+   },
+   "outputs": [],
+   "source": [
+    "import tensorflow_datasets as tfds\n",
+    "\n",
+    "dataset = tfds.load(\"horses_or_humans\", split=\"train\")\n",
+    "dataset = dataset.filter(lambda x: x[\"label\"] == 1)  # 1 for humans"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "7lbE11mbqIZs"
+   },
+   "source": [
+    "Let's visualize a few samples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 829
+    },
+    "id": "c17-lH8VVWjL",
+    "outputId": "2475751e-732c-48b6-efec-f85459de4e65"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x1000 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.figure(figsize=(10, 10))\n",
+    "for i, sample in enumerate(dataset.take(9)):\n",
+    "    ax = plt.subplot(3, 3, i + 1)\n",
+    "    plt.imshow(sample[\"image\"])\n",
+    "    plt.title(int(sample[\"label\"]))\n",
+    "    plt.axis(\"off\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8a6n-uLoqLxA"
+   },
+   "source": [
+    "As mentioned above, we are only interested in the samples that correspond to the `humans` label."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GnB9tYL4qUaO"
+   },
+   "source": [
+    "## Extract conditioning images with MediaPipe\n",
+    "\n",
+    "From the [official website](https://developers.google.com/mediapipe):\n",
+    "\n",
+    "> MediaPipe offers open source cross-platform, customizable ML solutions for live and streaming media.\n",
+    "\n",
+    "MediaPipe offers a bookeh of solutions that are particularly suitable for live and streaming media such as: selfie segmentation, face mesh, hand tracking, human pose estimation and tracking, etc. You can find out all the solutions supported by MediaPipe [here](https://developers.google.com/mediapipe/solutions/). \n",
+    "\n",
+    "We will use MediaPipe's APIs to extract pose body joints for ControlNet training.\n",
+    "\n",
+    "ControlNet training expects the following:\n",
+    "\n",
+    "* original images (like the ones above)\n",
+    "* corresponding conditioning images (segmented parts, pose estimation, Canny edges, etc.) \n",
+    "* captions that describe the original images\n",
+    "\n",
+    "In this example, we leverage [MediaPipe Pose Landmark Detection](https://developers.google.com/mediapipe/solutions/vision/pose_landmarker) to extract pose estimated body points from the original images. Some other MediaPipe Solutions that are relevant for ControlNet training include [Face Landmark Detection](https://developers.google.com/mediapipe/solutions/vision/face_landmarker) and [Selfie Segmentation](https://developers.google.com/mediapipe/solutions/vision/image_segmenter).\n",
+    "\n",
+    "For a detailed overview of how the ControlNet training workflow, refer to [this blog post](https://huggingface.co/blog/train-your-controlnet)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "i96e5GwprAPK"
+   },
+   "source": [
+    "Below, we write utilities to extract the pose estimated body points and draw them on blank images to serve as our conditioning images.  \n",
+    "\n",
+    "All MediaPipe Solutions Python API examples are under `mp.solutions`.\n",
+    "\n",
+    "For the MediaPipe Pose solution, we can access this module as `mp_pose = mp.solutions.pose`.\n",
+    "\n",
+    "You may change the parameters, such as `static_image_mode` and `min_detection_confidence`, during the initialization. Run `help(mp_pose.Pose)` to get more informations about the parameters. The official documentation for the `Pose` Python API is available [here](https://google.github.io/mediapipe/solutions/pose#python-solution-api)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "id": "8aXOF0R4VW2n"
+   },
+   "outputs": [],
+   "source": [
+    "import mediapipe as mp\n",
+    "\n",
+    "mp_drawing = mp.solutions.drawing_utils\n",
+    "mp_pose = mp.solutions.pose"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " We define two important variables:\n",
+    "\n",
+    "* `_JOINT_CMAP`: defines the body joint colors.\n",
+    "* `_CONNECTION_CMAP`: defines the connection (connection between the joints) colors. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "id": "JOK5RMiMWEAB"
+   },
+   "outputs": [],
+   "source": [
+    "# MediaPipe Pose annotation for ControlNet.\n",
+    "from typing import Any\n",
+    "import numpy as np\n",
+    "import math\n",
+    "import cv2\n",
+    "\n",
+    "# Body joint color map. #BGR\n",
+    "_JOINT_CMAP = {\n",
+    "    0: [0, 0, 255],\n",
+    "    1: [255, 208, 0],\n",
+    "    2: [255, 161, 0],\n",
+    "    3: [255, 114, 0],\n",
+    "    4: [0, 189, 255],\n",
+    "    5: [0, 236, 255],\n",
+    "    6: [0, 255, 226],\n",
+    "    7: [255, 0, 76],\n",
+    "    8: [0, 255, 131],\n",
+    "    9: [255, 0, 171],\n",
+    "    10: [0, 255, 37],\n",
+    "    11: [244, 0, 253],\n",
+    "    12: [57, 255, 0],\n",
+    "    13: [151, 0, 255],\n",
+    "    14: [151, 255, 0],\n",
+    "    15: [57, 0, 255],\n",
+    "    16: [245, 255, 0],\n",
+    "    17: [0, 39, 255],\n",
+    "    18: [255, 169, 0],\n",
+    "    19: [0, 133, 255],\n",
+    "    20: [255, 75, 0],\n",
+    "    21: [0, 228, 255],\n",
+    "    22: [255, 0, 19],\n",
+    "    23: [0, 255, 189],\n",
+    "    24: [255, 0, 113],\n",
+    "    25: [0, 255, 94],\n",
+    "    26: [255, 0, 208],\n",
+    "    27: [6, 255, 6],\n",
+    "    28: [207, 0, 255],\n",
+    "    29: [96, 255, 0],\n",
+    "    30: [112, 0, 255],\n",
+    "    31: [190, 255, 0],\n",
+    "    32: [23, 0, 255],\n",
+    "}\n",
+    "\n",
+    "\n",
+    "# Connection color map. #BGR\n",
+    "_CONNECTION_CMAP = {\n",
+    "    (0, 1): [127, 104, 127],\n",
+    "    (0, 4): [0, 94, 255],\n",
+    "    (1, 2): [255, 184, 0],\n",
+    "    (2, 3): [255, 137, 0],\n",
+    "    (3, 7): [255, 57, 38],\n",
+    "    (4, 5): [0, 212, 255],\n",
+    "    (5, 6): [0, 245, 240],\n",
+    "    (6, 8): [0, 255, 178],\n",
+    "    (9, 10): [127, 127, 104],\n",
+    "    (11, 12): [150, 127, 126],\n",
+    "    (11, 13): [197, 0, 254],\n",
+    "    (11, 23): [122, 127, 221],\n",
+    "    (12, 14): [104, 255, 0],\n",
+    "    (12, 24): [156, 127, 56],\n",
+    "    (13, 15): [104, 0, 255],\n",
+    "    (14, 16): [198, 255, 0],\n",
+    "    (15, 17): [28, 19, 255],\n",
+    "    (15, 19): [28, 66, 255],\n",
+    "    (15, 21): [28, 114, 255],\n",
+    "    (16, 18): [250, 212, 0],\n",
+    "    (16, 20): [250, 165, 0],\n",
+    "    (16, 22): [250, 127, 9],\n",
+    "    (17, 19): [0, 86, 255],\n",
+    "    (18, 20): [255, 122, 0],\n",
+    "    (23, 24): [127, 127, 151],\n",
+    "    (23, 25): [0, 255, 141],\n",
+    "    (24, 26): [255, 0, 160],\n",
+    "    (25, 27): [3, 255, 50],\n",
+    "    (26, 28): [231, 0, 231],\n",
+    "    (27, 29): [51, 255, 3],\n",
+    "    (27, 31): [98, 255, 3],\n",
+    "    (28, 30): [159, 0, 255],\n",
+    "    (28, 32): [115, 0, 255],\n",
+    "    (29, 31): [143, 255, 0],\n",
+    "    (30, 32): [67, 0, 255],\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we write a utility `draw_pose()` for:\n",
+    "\n",
+    "* Extracting landmarks from the landmark_list and store landmarks' pixel values in a map.\n",
+    "* Computing the mouth center and shoulder center, which are not in thelandmark_list but are needed in pose annotation.\n",
+    "* Drawing the connections between landmarks.\n",
+    "* Drawing the pose landmarks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def draw_pose(\n",
+    "    image: np.ndarray,\n",
+    "    landmark_list: Any,\n",
+    "    connections: Any,\n",
+    "    overlay: bool = True,\n",
+    ") -> tuple[np.ndarray, dict[str, list[float]]]:\n",
+    "    \"\"\"Draws the landmarks and the connections on the image.\n",
+    "\n",
+    "    Args:\n",
+    "      image: A three channel BGR image represented as numpy ndarray.\n",
+    "      landmark_list: A normalized landmark list proto message to be annotated on\n",
+    "        the image.\n",
+    "      connections: A list of landmark index tuples that specifies how landmarks to\n",
+    "        be connected in the drawing.\n",
+    "      overlay: Whether to overlay on the input image.\n",
+    "\n",
+    "    Returns:\n",
+    "      (image, dictionary).\n",
+    "\n",
+    "    Raises:\n",
+    "      ValueError: If one of the following:\n",
+    "        a) If the input image is not three channel BGR.\n",
+    "        b) If any connetions contain invalid landmark index.\n",
+    "    \"\"\"\n",
+    "    if image.shape[2] != mp_drawing._BGR_CHANNELS:\n",
+    "        raise ValueError(\"Input image must contain three channel bgr data.\")\n",
+    "    image_rows, image_cols, _ = image.shape\n",
+    "    min_length = min(image_rows, image_cols)\n",
+    "    draw_line_width = math.floor(min_length * 0.01)\n",
+    "    draw_circle_radius = math.floor(min_length * 0.015)\n",
+    "    idx_to_coordinates = {}\n",
+    "    visibility = {}\n",
+    "    mouth_center, shoulder_center = [], []\n",
+    "    flattened_landmarks = {\"coordinates\": [], \"visibility\": []}\n",
+    "\n",
+    "    for idx, landmark in enumerate(landmark_list.landmark):\n",
+    "        # Gets flattened landmarks as list.\n",
+    "        flattened_landmarks[\"coordinates\"].extend(\n",
+    "            [round(landmark.x, 2), round(landmark.y, 2), round(landmark.z, 2)]\n",
+    "        )\n",
+    "        flattened_landmarks[\"visibility\"].extend([round(landmark.visibility, 2)])\n",
+    "\n",
+    "        # Loose threshold.\n",
+    "        if (\n",
+    "            landmark.HasField(\"visibility\")\n",
+    "            and landmark.visibility < mp_drawing._VISIBILITY_THRESHOLD * 0.8\n",
+    "        ) or (\n",
+    "            landmark.HasField(\"presence\")\n",
+    "            and landmark.presence < mp_drawing._PRESENCE_THRESHOLD * 0.8\n",
+    "        ):\n",
+    "            continue\n",
+    "        landmark_px = mp_drawing._normalized_to_pixel_coordinates(\n",
+    "            landmark.x, landmark.y, image_cols, image_rows\n",
+    "        )\n",
+    "        if landmark_px:\n",
+    "            idx_to_coordinates[idx] = landmark_px\n",
+    "            visibility[idx] = landmark.visibility\n",
+    "\n",
+    "    if (\n",
+    "        mp_pose.PoseLandmark.MOUTH_LEFT in idx_to_coordinates\n",
+    "        and mp_pose.PoseLandmark.MOUTH_RIGHT in idx_to_coordinates\n",
+    "    ):\n",
+    "        for x, y in zip(\n",
+    "            idx_to_coordinates[mp_pose.PoseLandmark.MOUTH_LEFT],\n",
+    "            idx_to_coordinates[mp_pose.PoseLandmark.MOUTH_RIGHT],\n",
+    "        ):\n",
+    "            mouth_center.append((x + y) // 2)\n",
+    "\n",
+    "    if (\n",
+    "        mp_pose.PoseLandmark.LEFT_SHOULDER in idx_to_coordinates\n",
+    "        and mp_pose.PoseLandmark.RIGHT_SHOULDER in idx_to_coordinates\n",
+    "    ):\n",
+    "        for x, y in zip(\n",
+    "            idx_to_coordinates[mp_pose.PoseLandmark.LEFT_SHOULDER],\n",
+    "            idx_to_coordinates[mp_pose.PoseLandmark.RIGHT_SHOULDER],\n",
+    "        ):\n",
+    "            shoulder_center.append((x + y) // 2)\n",
+    "\n",
+    "    if overlay:\n",
+    "        output_image = image.copy()\n",
+    "    else:\n",
+    "        output_image = np.zeros(\n",
+    "            list(image.shape[:2])\n",
+    "            + [\n",
+    "                3,\n",
+    "            ],\n",
+    "            dtype=np.uint8,\n",
+    "        )\n",
+    "\n",
+    "    if connections:\n",
+    "        num_landmarks = len(landmark_list.landmark)\n",
+    "        # Draws the connections if the start and end landmarks are both visible.\n",
+    "        for connection in connections:\n",
+    "            start_idx = connection[0]\n",
+    "            end_idx = connection[1]\n",
+    "            if not (0 <= start_idx < num_landmarks and 0 <= end_idx < num_landmarks):\n",
+    "                raise ValueError(\n",
+    "                    \"Landmark index is out of range. Invalid connection \"\n",
+    "                    f\"from landmark #{start_idx} to landmark #{end_idx}.\"\n",
+    "                )\n",
+    "            if start_idx in idx_to_coordinates and end_idx in idx_to_coordinates:\n",
+    "                cv2.line(\n",
+    "                    output_image,\n",
+    "                    pt1=idx_to_coordinates[start_idx],\n",
+    "                    pt2=idx_to_coordinates[end_idx],\n",
+    "                    color=_CONNECTION_CMAP[(start_idx, end_idx)],\n",
+    "                    thickness=draw_line_width,\n",
+    "                )\n",
+    "\n",
+    "        # Draw mouth to neck\n",
+    "        if mouth_center and shoulder_center:\n",
+    "            cv2.line(\n",
+    "                output_image,\n",
+    "                pt1=tuple(mouth_center),\n",
+    "                pt2=tuple(shoulder_center),\n",
+    "                color=[255, 255, 255],\n",
+    "                thickness=draw_line_width,\n",
+    "            )\n",
+    "\n",
+    "    # Draws landmark points after finishing the connection lines, which is\n",
+    "    # aesthetically better.\n",
+    "    for idx, landmark_px in idx_to_coordinates.items():\n",
+    "        # Fill color into the circle\n",
+    "        cv2.circle(\n",
+    "            output_image,\n",
+    "            center=landmark_px,\n",
+    "            radius=draw_circle_radius,\n",
+    "            color=_JOINT_CMAP[idx],\n",
+    "            thickness=-1,\n",
+    "        )\n",
+    "\n",
+    "    return output_image, flattened_landmarks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "uzVPY2VksKJw"
+   },
+   "source": [
+    "## Map conditioning extraction\n",
+    "\n",
+    "Since `dataset` is already a `tf.data.Dataset` we can write another utility to map over `dataset`. This utility will give us:\n",
+    "\n",
+    "* the original image\n",
+    "* the body points overlaid on the original image\n",
+    "* the body points laid on a blank image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "id": "RrJTam06WE1M"
+   },
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "\n",
+    "\n",
+    "def determine_pose(image: tf.Tensor):\n",
+    "    \"\"\"Estimates pose and creates an image with just the pose body points.\n",
+    "\n",
+    "    The image consisting the pose body points serves as the conditioning image\n",
+    "    for ControlNet training.\n",
+    "\n",
+    "    Args:\n",
+    "        image: A three channel RGB image represented as a tf.Tensor.\n",
+    "\n",
+    "    Returns:\n",
+    "        A tuple consisting of the original image (`image`), an image where\n",
+    "        the original image is overlaid with the pose keypoints, and an image\n",
+    "        with just the pose keypoints.\n",
+    "    \"\"\"\n",
+    "    image = image.numpy()\n",
+    "\n",
+    "    # Run MediaPipe Pose and draw pose landmarks.\n",
+    "    with mp_pose.Pose(\n",
+    "        static_image_mode=True, min_detection_confidence=0.5, model_complexity=2\n",
+    "    ) as pose:\n",
+    "        # Process it with MediaPipe Pose.\n",
+    "        results = pose.process(image)\n",
+    "\n",
+    "        # Draw pose landmarks on a copy of the input image.\n",
+    "        annotated_image, flattened_landmarks = draw_pose(\n",
+    "            image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS\n",
+    "        )\n",
+    "\n",
+    "        # Draw pose landmarks on a blank image.\n",
+    "        blank_image, flattened_landmarks = draw_pose(\n",
+    "            image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS, False\n",
+    "        )\n",
+    "\n",
+    "    return image, annotated_image, blank_image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "iyqyL9xpXTkq",
+    "outputId": "b6a8cf79-a164-422b-9589-47609a3cd14f"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From <ipython-input-6-61175a2bda17>:8: ignore_errors (from tensorflow.python.data.experimental.ops.error_ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use `tf.data.Dataset.ignore_errors` instead.\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset = dataset.map(lambda x: x[\"image\"], num_parallel_calls=tf.data.AUTOTUNE)\n",
+    "dataset = dataset.map(\n",
+    "    lambda image: (\n",
+    "        tf.py_function(determine_pose, [image], [tf.uint8, tf.uint8, tf.uint8])\n",
+    "    )\n",
+    ")\n",
+    "dataset = dataset.apply(tf.data.experimental.ignore_errors())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Qu8F-B9urxJz"
+   },
+   "source": [
+    "## Visualize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "rX7WHH7nXabL",
+    "outputId": "2bdd9bdb-ca85-4b60-f92b-55b456d2ee63"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 500x1500 with 27 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_samples_to_vis = 9\n",
+    "fig, ax = plt.subplots(ncols=3, nrows=num_samples_to_vis, figsize=(5, 15))\n",
+    "for i, sample in enumerate(dataset.take(num_samples_to_vis).as_numpy_iterator()):\n",
+    "    original_image = sample[0]\n",
+    "    annotated_image, blank_image = sample[1], sample[2]\n",
+    "\n",
+    "    samples = [original_image, annotated_image, blank_image]\n",
+    "    titles = [\"Input\", \"Annotation Overlaid\", \"Annotation\"]\n",
+    "\n",
+    "    for j in range(3):\n",
+    "        ax[i, j].imshow(samples[j])\n",
+    "        ax[i, j].axis(\"off\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "xmMaQsJTw8FL"
+   },
+   "source": [
+    "## Caption generation with BLIP-2\n",
+    "\n",
+    "For this section, we leverage an open-source and powerful pre-trained model for caption generation - [BLIP-2](https://huggingface.co/papers/2301.12597). This model comes with [different checkpoints](https://huggingface.co/models?other=blip-2), among which we're using [Salesforce/blip2-flan-t5-xl](https://huggingface.co/Salesforce/blip2-flan-t5-xl). \n",
+    "\n",
+    "🤗 Transformers also supports its predecessor - [BLIP](https://huggingface.co/papers/2201.12086). \n",
+    "\n",
+    "In the cell below, we first load the following classes: `Blip2Processor` and `Blip2ForConditionalGeneration`. Then we write a utility to generate captions for a given batch of images. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 49,
+     "referenced_widgets": [
+      "f90f1c122f39450c86883ef808df91a3",
+      "04e34bf48e0d4d75bb85d593aaefb472",
+      "f8641a16fb954f4ea71245dd60c1068d",
+      "0ee38e6e5d7840a2a61192b8525ca224",
+      "030606273c1b44deac4151a7e722f14a",
+      "31112b3d02b645708a5edd5573a4c951",
+      "272813e80d8349518f6f54eed9e40aa4",
+      "3283137ecb3b45d3807fa2a920a6fe79",
+      "70a5f206f22c4bfd98676ca54232a1e4",
+      "08137370444343348a5333f04a8907d4",
+      "cfbcb4aa9ed04496a115e018a5b53708"
+     ]
+    },
+    "id": "W2qGNAcnXunS",
+    "outputId": "95cf3cee-a5fb-4044-978c-36db7edcd325"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f90f1c122f39450c86883ef808df91a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from transformers import Blip2Processor, Blip2ForConditionalGeneration\n",
+    "import torch\n",
+    "\n",
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "\n",
+    "processor = Blip2Processor.from_pretrained(\"Salesforce/blip2-flan-t5-xl\")\n",
+    "captioning_model = Blip2ForConditionalGeneration.from_pretrained(\n",
+    "    \"Salesforce/blip2-flan-t5-xl\", torch_dtype=torch.float16\n",
+    ")\n",
+    "captioning_model = captioning_model.to(device)\n",
+    "\n",
+    "\n",
+    "def generation_captions(images: np.ndarray) -> list[str]:\n",
+    "    \"\"\"Generates captions for a batch of images.\n",
+    "\n",
+    "    Args:\n",
+    "        images: A batch of images in the RGB format.\n",
+    "\n",
+    "    Returns:\n",
+    "        A list of generated captions.\n",
+    "    \"\"\"\n",
+    "    inputs = processor(images=images, return_tensors=\"pt\").to(device, torch.float16)\n",
+    "\n",
+    "    generated_ids = captioning_model.generate(**inputs)\n",
+    "    generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True)\n",
+    "    generated_texts = [text.strip() for text in generated_texts]\n",
+    "    return generated_texts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "vtKtHGclyHds"
+   },
+   "source": [
+    "## Generate captions and serialize\n",
+    "\n",
+    "In this section, we first batch-generate captions and then we serialize the original images, and their corresponding conditioning images."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "VovixW1_bhg-",
+    "outputId": "a1287d7b-950f-4b08-9874-1251c787e396"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.9/dist-packages/transformers/generation/utils.py:1288: UserWarning: Using `max_length`'s default (20) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "496 samples will go in the final dataset.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import hashlib\n",
+    "import PIL\n",
+    "\n",
+    "batch_size = 16\n",
+    "data_root = \"data\"\n",
+    "dataset = dataset.batch(batch_size, drop_remainder=True)\n",
+    "\n",
+    "os.makedirs(data_root, exist_ok=True)\n",
+    "\n",
+    "\n",
+    "def save_image(\n",
+    "    original_image: np.ndarray,\n",
+    "    overlaid_annotation: np.ndarray,\n",
+    "    blank_annotation: np.ndarray,\n",
+    "):\n",
+    "    \"\"\"Serializes images to `data_root`.\"\"\"\n",
+    "    image_hash = hashlib.sha1(original_image.tobytes()).hexdigest()\n",
+    "    PIL.Image.fromarray(original_image).save(\n",
+    "        os.path.join(data_root, f\"{str(image_hash)}_original.png\")\n",
+    "    )\n",
+    "    PIL.Image.fromarray(overlaid_annotation).save(\n",
+    "        os.path.join(data_root, f\"{str(image_hash)}_overlaid.png\")\n",
+    "    )\n",
+    "    PIL.Image.fromarray(blank_annotation).save(\n",
+    "        os.path.join(data_root, f\"{str(image_hash)}_condition.png\")\n",
+    "    )\n",
+    "    return image_hash\n",
+    "\n",
+    "\n",
+    "original_image_paths = []\n",
+    "overlaid_annotation_paths = []\n",
+    "blank_annotation_paths = []\n",
+    "all_generated_captions = []\n",
+    "total = 0\n",
+    "\n",
+    "for samples in dataset.as_numpy_iterator():\n",
+    "    original_images = samples[0]\n",
+    "    overlaid_annotations = samples[1]\n",
+    "    blank_annotations = samples[2]\n",
+    "\n",
+    "    generated_captions = generation_captions(original_images)\n",
+    "\n",
+    "    for i in range(len(original_images)):\n",
+    "        image_hash = save_image(\n",
+    "            original_images[i], overlaid_annotations[i], blank_annotations[i]\n",
+    "        )\n",
+    "        original_image_paths.append(\n",
+    "            os.path.join(data_root, f\"{str(image_hash)}_original.png\")\n",
+    "        )\n",
+    "        overlaid_annotation_paths.append(\n",
+    "            os.path.join(data_root, f\"{str(image_hash)}_overlaid.png\")\n",
+    "        )\n",
+    "        blank_annotation_paths.append(\n",
+    "            os.path.join(data_root, f\"{str(image_hash)}_condition.png\")\n",
+    "        )\n",
+    "\n",
+    "        all_generated_captions.append(generated_captions[i])\n",
+    "\n",
+    "    total += len(original_images)\n",
+    "\n",
+    "print(f\"{total} samples will go in the final dataset.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "qKiC4evYyaDb"
+   },
+   "source": [
+    "## Create a 🤗 Dataset and push to the Hub\n",
+    "\n",
+    "For this section, we leverage 🤗 Datasets to create a the final dataset that is ready to go with our [ControlNet training script](https://github.com/huggingface/diffusers/blob/main/examples/controlnet/train_controlnet_flax.py). \n",
+    "\n",
+    "Finally, we push the prepared dataset to Hub for easy sharing with the community. To be able to do that, you need to be a registered Hugging Face user to authenticate yourself. If you are not one already, please head over [hf.co](https://hf.co) and register youself; it's free =) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "WkGGxZjgzSOj",
+    "outputId": "d153f0a2-ff10-4cdd-91a9-5c4ec6cc08f1"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
+      "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
+      "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
+      "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
+      "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
+      "    \n",
+      "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
+      "Token: \n",
+      "Add token as git credential? (Y/n) Y\n",
+      "Token is valid.\n",
+      "\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine.\n",
+      "You might have to re-authenticate when pushing to the Hugging Face Hub.\n",
+      "Run the following command in your terminal in case you want to set the 'store' credential helper as default.\n",
+      "\n",
+      "git config --global credential.helper store\n",
+      "\n",
+      "Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001b[0m\n",
+      "Token has not been saved to git credential helper.\n",
+      "Your token has been saved to /root/.cache/huggingface/token\n",
+      "Login successful\n"
+     ]
+    }
+   ],
+   "source": [
+    "!huggingface-cli login"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "VEvALWiZ1agR"
+   },
+   "source": [
+    "We start with a generator that yields a tuple consisting of:\n",
+    "\n",
+    "* `original_image`\n",
+    "* `condtioning_image`\n",
+    "* `overlaid`\n",
+    "* `caption`\n",
+    "\n",
+    "Note that `overlaid` image is just there for convenience. It is not **mandatory** for us to include it while creating the dataset as it won't be used during training. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "id": "ZiNk_gOYze4Q"
+   },
+   "outputs": [],
+   "source": [
+    "def gen_examples():\n",
+    "    for i in range(len(original_image_paths)):\n",
+    "        yield {\n",
+    "            \"original_image\": {\"path\": original_image_paths[i]},\n",
+    "            \"condtioning_image\": {\"path\": blank_annotation_paths[i]},\n",
+    "            \"overlaid\": {\"path\": overlaid_annotation_paths[i]},\n",
+    "            \"caption\": all_generated_captions[i],\n",
+    "        }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 218,
+     "referenced_widgets": [
+      "f24f39de5e8f4e218d3f0c414b731bf6",
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+      "a4ec7a1577b1476f8177bdf56ea5370d",
+      "e4b03ce4c56049fdbe4c45e082a046a7",
+      "f93250a20f35446bba13bf79d6ae0791",
+      "a9bff6ee4a31496392eaaafaba46b3c9",
+      "d57cac5c01e643d79a8a505d7e124beb",
+      "758acba7b1aa44f8a760d0248e07b8f9",
+      "a912fa34318a4c8a85e45e3bfa33b5f9",
+      "6b971b373fb84caa9c355ae58fd2d178",
+      "fca696678a97472cbe2cbda527c8a7e4",
+      "74eed040b0624110a48b1cb46c124d68",
+      "ae25a42fe79b426bab94899b3089b59b",
+      "80497f7dd515449b989417bee32cf12b"
+     ]
+    },
+    "id": "zkfsT6uq07TB",
+    "outputId": "676de93f-5a37-459a-c1dd-c5283cf0ba9b"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:datasets.builder:Setting num_proc from 8 back to 1 for the train split to disable multiprocessing as it only contains one shard.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading and preparing dataset generator/default to /root/.cache/huggingface/datasets/generator/default-c340e697ab6fb52e/0.0.0...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f24f39de5e8f4e218d3f0c414b731bf6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating train split: 0 examples [00:00, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset generator downloaded and prepared to /root/.cache/huggingface/datasets/generator/default-c340e697ab6fb52e/0.0.0. Subsequent calls will reuse this data.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d85cb5294f69414b96e7673888a6148e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map:   0%|          | 0/496 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d16e5742d98641b8996ff8ab05656407",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Pushing dataset shards to the dataset hub:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "39d1228ff9034ff6b4d24e91ad89ecd2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Creating parquet from Arrow format:   0%|          | 0/1 [00:00<?, ?ba/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e4b03ce4c56049fdbe4c45e082a046a7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Upload 1 LFS files:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from datasets import Dataset, Features\n",
+    "from datasets import Image as ImageFeature\n",
+    "from datasets import Value\n",
+    "\n",
+    "final_dataset = Dataset.from_generator(\n",
+    "    gen_examples,\n",
+    "    features=Features(\n",
+    "        original_image=ImageFeature(),\n",
+    "        condtioning_image=ImageFeature(),\n",
+    "        overlaid=ImageFeature(),\n",
+    "        caption=Value(\"string\"),\n",
+    "    ),\n",
+    "    num_proc=8,\n",
+    ")\n",
+    "\n",
+    "ds_name = \"poses-controlnet-dataset\"\n",
+    "final_dataset.push_to_hub(ds_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ghbqWY0a16oO",
+    "outputId": "ccaf9406-a5eb-4484-a4cc-67b4b2620fc3"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(496, 496, 496, 496)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(original_image_paths), len(blank_annotation_paths), len(overlaid_annotation_paths), len(all_generated_captions)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "a8A8PP6l1Ngp"
+   },
+   "source": [
+    "Refer to [`from_generator()` documentation](https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Dataset.from_generator) to know more about it. \n",
+    "\n",
+    "Note that, you can specify `private=True` in `push_to_hub` to host the dataset privately. \n",
+    "\n",
+    "After the dataset is pushed, it will look like so:\n",
+    "\n",
+    "![](https://i.imgur.com/Xi6iLal.png)\n",
+    "\n",
+    "You can find the final dataset here: [sayakpaul/poses-controlnet-dataset](https://huggingface.co/datasets/sayakpaul/poses-controlnet-dataset). \n",
+    "\n",
+    "We can now successfully train a ControlNet model. Refer to [this section](https://github.com/huggingface/diffusers/tree/main/examples/controlnet#training-with-flaxjax) for more details. "
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "machine_shape": "hm",
+   "provenance": []
+  },
+  "gpuClass": "premium",
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.2"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "001d75de3bf140ac9465cd6de0731d2d": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HTMLModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HTMLModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HTMLView",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_ac39cd7b5d3d4d5d9428c175e48f95eb",
+      "placeholder": "​",
+      "style": "IPY_MODEL_905f066f87b647f597116a466d1e0bdc",
+      "value": " 0/0 [00:00&lt;?, ? examples/s]"
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+      "_view_count": null,
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+      "_view_module_version": "1.2.0",
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+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
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+      "overflow": null,
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+      "overflow_y": null,
+      "padding": null,
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+      "top": null,
+      "visibility": null,
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+     "state": {
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+}
diff --git a/jax-controlnet-sprint/dataset_tools/data.py b/jax-controlnet-sprint/dataset_tools/data.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a570f45991c4f5314df75dfc84941958d5bfb9c
--- /dev/null
+++ b/jax-controlnet-sprint/dataset_tools/data.py
@@ -0,0 +1,86 @@
+import os
+
+import datasets
+import pandas as pd
+
+_VERSION = datasets.Version("0.0.2")
+
+_DESCRIPTION = "TODO"
+_HOMEPAGE = "TODO"
+_LICENSE = "TODO"
+_CITATION = "TODO"
+
+_FEATURES = datasets.Features(
+    {
+        "image": datasets.Image(),
+        "conditioning_image": datasets.Image(),
+        "text": datasets.Value("string"),
+    },
+)
+
+
+_DEFAULT_CONFIG = datasets.BuilderConfig(name="default", version=_VERSION)
+DATA_DIR = "/mnt/disks/persist/data"
+
+
+class coyo(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [_DEFAULT_CONFIG]
+    DEFAULT_CONFIG_NAME = "default"
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=_FEATURES,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        metadata_path = f"{DATA_DIR}/meta.jsonl"
+        images_dir = f"{DATA_DIR}/images"
+        conditioning_images_dir = f"{DATA_DIR}/processed_images"
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "metadata_path": metadata_path,
+                    "images_dir": images_dir,
+                    "conditioning_images_dir": conditioning_images_dir,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, metadata_path, images_dir, conditioning_images_dir):
+        metadata = pd.read_json(metadata_path, lines=True)
+
+        for _, row in metadata.iterrows():
+            text = row["caption"]
+
+            try:
+                image_path = row["image"]
+                image_path = os.path.join(images_dir, image_path)
+                image = open(image_path, "rb").read()
+
+                conditioning_image_path = row["conditioning_image"]
+                conditioning_image_path = os.path.join(
+                    conditioning_images_dir, row["conditioning_image"]
+                )
+                conditioning_image = open(conditioning_image_path, "rb").read()
+
+                yield row["image"], {
+                    "text": text,
+                    "image": {
+                        "path": image_path,
+                        "bytes": image,
+                    },
+                    "conditioning_image": {
+                        "path": conditioning_image_path,
+                        "bytes": conditioning_image,
+                    },
+                }
+            except Exception as e:
+                print(e)
diff --git a/jax-controlnet-sprint/training_scripts/requirements_flax.txt b/jax-controlnet-sprint/training_scripts/requirements_flax.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f194e8bf6b0fdc83c6936d2a585e17f9d2bf49ff
--- /dev/null
+++ b/jax-controlnet-sprint/training_scripts/requirements_flax.txt
@@ -0,0 +1,14 @@
+-f https://storage.googleapis.com/jax-releases/libtpu_releases.html
+jax[tpu]==0.4.5
+flax==0.6.7
+transformers
+chex
+datasets
+optax
+orbax
+ftfy
+tensorboard
+Jinja2
+--extra-index-url https://download.pytorch.org/whl/cpu
+torch
+torchvision
diff --git a/jax-controlnet-sprint/training_scripts/train_controlnet_flax.py b/jax-controlnet-sprint/training_scripts/train_controlnet_flax.py
new file mode 100644
index 0000000000000000000000000000000000000000..24b32e7f4301affa9242cebdd18e98df086400a5
--- /dev/null
+++ b/jax-controlnet-sprint/training_scripts/train_controlnet_flax.py
@@ -0,0 +1,1142 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+
+import argparse
+import logging
+import math
+import os
+import random
+import time
+from pathlib import Path
+
+import jax
+import jax.numpy as jnp
+import numpy as np
+import optax
+import torch
+import torch.utils.checkpoint
+import transformers
+from datasets import load_dataset, load_from_disk
+from flax import jax_utils
+from flax.core.frozen_dict import unfreeze
+from flax.training import train_state
+from flax.training.common_utils import shard
+from huggingface_hub import create_repo, upload_folder
+from PIL import Image, PngImagePlugin
+from torch.utils.data import IterableDataset
+from torchvision import transforms
+from tqdm.auto import tqdm
+from transformers import CLIPTokenizer, FlaxCLIPTextModel, set_seed
+
+from diffusers import (
+    FlaxAutoencoderKL,
+    FlaxControlNetModel,
+    FlaxDDPMScheduler,
+    FlaxStableDiffusionControlNetPipeline,
+    FlaxUNet2DConditionModel,
+)
+from diffusers.utils import check_min_version, is_wandb_available
+
+
+# To prevent an error that occurs when there are abnormally large compressed data chunk in the png image
+# see more https://github.com/python-pillow/Pillow/issues/5610
+LARGE_ENOUGH_NUMBER = 100
+PngImagePlugin.MAX_TEXT_CHUNK = LARGE_ENOUGH_NUMBER * (1024**2)
+
+if is_wandb_available():
+    import wandb
+
+# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
+check_min_version("0.16.0.dev0")
+
+logger = logging.getLogger(__name__)
+
+
+def image_grid(imgs, rows, cols):
+    assert len(imgs) == rows * cols
+
+    w, h = imgs[0].size
+    grid = Image.new("RGB", size=(cols * w, rows * h))
+    grid_w, grid_h = grid.size
+
+    for i, img in enumerate(imgs):
+        grid.paste(img, box=(i % cols * w, i // cols * h))
+    return grid
+
+
+def log_validation(pipeline, pipeline_params, controlnet_params, tokenizer, args, rng, weight_dtype):
+    logger.info("Running validation...")
+
+    pipeline_params = pipeline_params.copy()
+    pipeline_params["controlnet"] = controlnet_params
+
+    num_samples = jax.device_count()
+    prng_seed = jax.random.split(rng, jax.device_count())
+
+    if len(args.validation_image) == len(args.validation_prompt):
+        validation_images = args.validation_image
+        validation_prompts = args.validation_prompt
+    elif len(args.validation_image) == 1:
+        validation_images = args.validation_image * len(args.validation_prompt)
+        validation_prompts = args.validation_prompt
+    elif len(args.validation_prompt) == 1:
+        validation_images = args.validation_image
+        validation_prompts = args.validation_prompt * len(args.validation_image)
+    else:
+        raise ValueError(
+            "number of `args.validation_image` and `args.validation_prompt` should be checked in `parse_args`"
+        )
+
+    image_logs = []
+
+    for validation_prompt, validation_image in zip(validation_prompts, validation_images):
+        prompts = num_samples * [validation_prompt]
+        prompt_ids = pipeline.prepare_text_inputs(prompts)
+        prompt_ids = shard(prompt_ids)
+
+        validation_image = Image.open(validation_image).convert("RGB")
+        processed_image = pipeline.prepare_image_inputs(num_samples * [validation_image])
+        processed_image = shard(processed_image)
+        images = pipeline(
+            prompt_ids=prompt_ids,
+            image=processed_image,
+            params=pipeline_params,
+            prng_seed=prng_seed,
+            num_inference_steps=50,
+            jit=True,
+        ).images
+
+        images = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
+        images = pipeline.numpy_to_pil(images)
+
+        image_logs.append(
+            {"validation_image": validation_image, "images": images, "validation_prompt": validation_prompt}
+        )
+
+    if args.report_to == "wandb":
+        formatted_images = []
+        for log in image_logs:
+            images = log["images"]
+            validation_prompt = log["validation_prompt"]
+            validation_image = log["validation_image"]
+
+            formatted_images.append(wandb.Image(validation_image, caption="Controlnet conditioning"))
+            for image in images:
+                image = wandb.Image(image, caption=validation_prompt)
+                formatted_images.append(image)
+
+        wandb.log({"validation": formatted_images})
+    else:
+        logger.warn(f"image logging not implemented for {args.report_to}")
+
+    return image_logs
+
+
+def save_model_card(repo_id: str, image_logs=None, base_model=str, repo_folder=None):
+    img_str = ""
+    if image_logs is not None:
+        for i, log in enumerate(image_logs):
+            images = log["images"]
+            validation_prompt = log["validation_prompt"]
+            validation_image = log["validation_image"]
+            validation_image.save(os.path.join(repo_folder, "image_control.png"))
+            img_str += f"prompt: {validation_prompt}\n"
+            images = [validation_image] + images
+            image_grid(images, 1, len(images)).save(os.path.join(repo_folder, f"images_{i}.png"))
+            img_str += f"![images_{i})](./images_{i}.png)\n"
+
+    yaml = f"""
+---
+license: creativeml-openrail-m
+base_model: {base_model}
+tags:
+- stable-diffusion
+- stable-diffusion-diffusers
+- text-to-image
+- diffusers
+- controlnet
+- jax-diffusers-event
+inference: true
+---
+    """
+    model_card = f"""
+# controlnet- {repo_id}
+
+These are controlnet weights trained on {base_model} with new type of conditioning. You can find some example images in the following. \n
+{img_str}
+"""
+    with open(os.path.join(repo_folder, "README.md"), "w") as f:
+        f.write(yaml + model_card)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Simple example of a training script.")
+    parser.add_argument(
+        "--pretrained_model_name_or_path",
+        type=str,
+        required=True,
+        help="Path to pretrained model or model identifier from huggingface.co/models.",
+    )
+    parser.add_argument(
+        "--controlnet_model_name_or_path",
+        type=str,
+        default=None,
+        help="Path to pretrained controlnet model or model identifier from huggingface.co/models."
+        " If not specified controlnet weights are initialized from unet.",
+    )
+    parser.add_argument(
+        "--revision",
+        type=str,
+        default=None,
+        help="Revision of pretrained model identifier from huggingface.co/models.",
+    )
+    parser.add_argument(
+        "--from_pt",
+        action="store_true",
+        help="Load the pretrained model from a PyTorch checkpoint.",
+    )
+    parser.add_argument(
+        "--controlnet_revision",
+        type=str,
+        default=None,
+        help="Revision of controlnet model identifier from huggingface.co/models.",
+    )
+    parser.add_argument(
+        "--profile_steps",
+        type=int,
+        default=0,
+        help="How many training steps to profile in the beginning.",
+    )
+    parser.add_argument(
+        "--profile_validation",
+        action="store_true",
+        help="Whether to profile the (last) validation.",
+    )
+    parser.add_argument(
+        "--profile_memory",
+        action="store_true",
+        help="Whether to dump an initial (before training loop) and a final (at program end) memory profile.",
+    )
+    parser.add_argument(
+        "--ccache",
+        type=str,
+        default=None,
+        help="Enables compilation cache.",
+    )
+    parser.add_argument(
+        "--controlnet_from_pt",
+        action="store_true",
+        help="Load the controlnet model from a PyTorch checkpoint.",
+    )
+    parser.add_argument(
+        "--tokenizer_name",
+        type=str,
+        default=None,
+        help="Pretrained tokenizer name or path if not the same as model_name",
+    )
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default="runs/{timestamp}",
+        help="The output directory where the model predictions and checkpoints will be written. "
+        "Can contain placeholders: {timestamp}.",
+    )
+    parser.add_argument(
+        "--cache_dir",
+        type=str,
+        default=None,
+        help="The directory where the downloaded models and datasets will be stored.",
+    )
+    parser.add_argument("--seed", type=int, default=0, help="A seed for reproducible training.")
+    parser.add_argument(
+        "--resolution",
+        type=int,
+        default=512,
+        help=(
+            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
+            " resolution"
+        ),
+    )
+    parser.add_argument(
+        "--train_batch_size", type=int, default=1, help="Batch size (per device) for the training dataloader."
+    )
+    parser.add_argument("--num_train_epochs", type=int, default=100)
+    parser.add_argument(
+        "--max_train_steps",
+        type=int,
+        default=None,
+        help="Total number of training steps to perform.",
+    )
+    parser.add_argument(
+        "--checkpointing_steps",
+        type=int,
+        default=5000,
+        help=("Save a checkpoint of the training state every X updates."),
+    )
+    parser.add_argument(
+        "--learning_rate",
+        type=float,
+        default=1e-4,
+        help="Initial learning rate (after the potential warmup period) to use.",
+    )
+    parser.add_argument(
+        "--scale_lr",
+        action="store_true",
+        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
+    )
+    parser.add_argument(
+        "--lr_scheduler",
+        type=str,
+        default="constant",
+        help=(
+            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
+            ' "constant", "constant_with_warmup"]'
+        ),
+    )
+    parser.add_argument(
+        "--snr_gamma",
+        type=float,
+        default=None,
+        help="SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. "
+        "More details here: https://arxiv.org/abs/2303.09556.",
+    )
+    parser.add_argument(
+        "--dataloader_num_workers",
+        type=int,
+        default=0,
+        help=(
+            "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
+        ),
+    )
+    parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
+    parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
+    parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
+    parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
+    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
+    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
+    parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
+    parser.add_argument(
+        "--hub_model_id",
+        type=str,
+        default=None,
+        help="The name of the repository to keep in sync with the local `output_dir`.",
+    )
+    parser.add_argument(
+        "--logging_steps",
+        type=int,
+        default=100,
+        help=("log training metric every X steps to `--report_t`"),
+    )
+    parser.add_argument(
+        "--report_to",
+        type=str,
+        default="wandb",
+        help=('The integration to report the results and logs to. Currently only supported platforms are `"wandb"`'),
+    )
+    parser.add_argument(
+        "--mixed_precision",
+        type=str,
+        default="no",
+        choices=["no", "fp16", "bf16"],
+        help=(
+            "Whether to use mixed precision. Choose"
+            "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
+            "and an Nvidia Ampere GPU."
+        ),
+    )
+    parser.add_argument(
+        "--dataset_name",
+        type=str,
+        default=None,
+        help=(
+            "The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
+            " dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
+            " or to a folder containing files that 🤗 Datasets can understand."
+        ),
+    )
+    parser.add_argument("--streaming", action="store_true", help="To stream a large dataset from Hub.")
+    parser.add_argument(
+        "--dataset_config_name",
+        type=str,
+        default=None,
+        help="The config of the Dataset, leave as None if there's only one config.",
+    )
+    parser.add_argument(
+        "--train_data_dir",
+        type=str,
+        default=None,
+        help=(
+            "A folder containing the training dataset. By default it will use `load_dataset` method to load a custom dataset from the folder."
+            "Folder must contain a dataset script as described here https://huggingface.co/docs/datasets/dataset_script) ."
+            "If `--load_from_disk` flag is passed, it will use `load_from_disk` method instead. Ignored if `dataset_name` is specified."
+        ),
+    )
+    parser.add_argument(
+        "--load_from_disk",
+        action="store_true",
+        help=(
+            "If True, will load a dataset that was previously saved using `save_to_disk` from `--train_data_dir`"
+            "See more https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.load_from_disk"
+        ),
+    )
+    parser.add_argument(
+        "--image_column", type=str, default="image", help="The column of the dataset containing the target image."
+    )
+    parser.add_argument(
+        "--conditioning_image_column",
+        type=str,
+        default="conditioning_image",
+        help="The column of the dataset containing the controlnet conditioning image.",
+    )
+    parser.add_argument(
+        "--caption_column",
+        type=str,
+        default="text",
+        help="The column of the dataset containing a caption or a list of captions.",
+    )
+    parser.add_argument(
+        "--max_train_samples",
+        type=int,
+        default=None,
+        help=(
+            "For debugging purposes or quicker training, truncate the number of training examples to this "
+            "value if set. Needed if `streaming` is set to True."
+        ),
+    )
+    parser.add_argument(
+        "--proportion_empty_prompts",
+        type=float,
+        default=0,
+        help="Proportion of image prompts to be replaced with empty strings. Defaults to 0 (no prompt replacement).",
+    )
+    parser.add_argument(
+        "--validation_prompt",
+        type=str,
+        default=None,
+        nargs="+",
+        help=(
+            "A set of prompts evaluated every `--validation_steps` and logged to `--report_to`."
+            " Provide either a matching number of `--validation_image`s, a single `--validation_image`"
+            " to be used with all prompts, or a single prompt that will be used with all `--validation_image`s."
+        ),
+    )
+    parser.add_argument(
+        "--validation_image",
+        type=str,
+        default=None,
+        nargs="+",
+        help=(
+            "A set of paths to the controlnet conditioning image be evaluated every `--validation_steps`"
+            " and logged to `--report_to`. Provide either a matching number of `--validation_prompt`s, a"
+            " a single `--validation_prompt` to be used with all `--validation_image`s, or a single"
+            " `--validation_image` that will be used with all `--validation_prompt`s."
+        ),
+    )
+    parser.add_argument(
+        "--validation_steps",
+        type=int,
+        default=100,
+        help=(
+            "Run validation every X steps. Validation consists of running the prompt"
+            " `args.validation_prompt` and logging the images."
+        ),
+    )
+    parser.add_argument("--wandb_entity", type=str, default=None, help=("The wandb entity to use (for teams)."))
+    parser.add_argument(
+        "--tracker_project_name",
+        type=str,
+        default="train_controlnet_flax",
+        help=("The `project` argument passed to wandb"),
+    )
+    parser.add_argument(
+        "--gradient_accumulation_steps", type=int, default=1, help="Number of steps to accumulate gradients over"
+    )
+    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
+
+    args = parser.parse_args()
+    args.output_dir = args.output_dir.replace("{timestamp}", time.strftime("%Y%m%d_%H%M%S"))
+
+    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
+    if env_local_rank != -1 and env_local_rank != args.local_rank:
+        args.local_rank = env_local_rank
+
+    # Sanity checks
+    if args.dataset_name is None and args.train_data_dir is None:
+        raise ValueError("Need either a dataset name or a training folder.")
+    if args.dataset_name is not None and args.train_data_dir is not None:
+        raise ValueError("Specify only one of `--dataset_name` or `--train_data_dir`")
+
+    if args.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1:
+        raise ValueError("`--proportion_empty_prompts` must be in the range [0, 1].")
+
+    if args.validation_prompt is not None and args.validation_image is None:
+        raise ValueError("`--validation_image` must be set if `--validation_prompt` is set")
+
+    if args.validation_prompt is None and args.validation_image is not None:
+        raise ValueError("`--validation_prompt` must be set if `--validation_image` is set")
+
+    if (
+        args.validation_image is not None
+        and args.validation_prompt is not None
+        and len(args.validation_image) != 1
+        and len(args.validation_prompt) != 1
+        and len(args.validation_image) != len(args.validation_prompt)
+    ):
+        raise ValueError(
+            "Must provide either 1 `--validation_image`, 1 `--validation_prompt`,"
+            " or the same number of `--validation_prompt`s and `--validation_image`s"
+        )
+
+    # This idea comes from
+    # https://github.com/borisdayma/dalle-mini/blob/d2be512d4a6a9cda2d63ba04afc33038f98f705f/src/dalle_mini/data.py#L370
+    if args.streaming and args.max_train_samples is None:
+        raise ValueError("You must specify `max_train_samples` when using dataset streaming.")
+
+    return args
+
+
+def make_train_dataset(args, tokenizer, batch_size=None):
+    # Get the datasets: you can either provide your own training and evaluation files (see below)
+    # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
+
+    # In distributed training, the load_dataset function guarantees that only one local process can concurrently
+    # download the dataset.
+    if args.dataset_name is not None:
+        # Downloading and loading a dataset from the hub.
+        dataset = load_dataset(
+            args.dataset_name,
+            args.dataset_config_name,
+            cache_dir=args.cache_dir,
+            streaming=args.streaming,
+        )
+    else:
+        if args.train_data_dir is not None:
+            if args.load_from_disk:
+                dataset = load_from_disk(
+                    args.train_data_dir,
+                )
+            else:
+                dataset = load_dataset(
+                    args.train_data_dir,
+                    cache_dir=args.cache_dir,
+                )
+        # See more about loading custom images at
+        # https://huggingface.co/docs/datasets/v2.0.0/en/dataset_script
+
+    # Preprocessing the datasets.
+    # We need to tokenize inputs and targets.
+    if isinstance(dataset["train"], IterableDataset):
+        column_names = next(iter(dataset["train"])).keys()
+    else:
+        column_names = dataset["train"].column_names
+
+    # 6. Get the column names for input/target.
+    if args.image_column is None:
+        image_column = column_names[0]
+        logger.info(f"image column defaulting to {image_column}")
+    else:
+        image_column = args.image_column
+        if image_column not in column_names:
+            raise ValueError(
+                f"`--image_column` value '{args.image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
+            )
+
+    if args.caption_column is None:
+        caption_column = column_names[1]
+        logger.info(f"caption column defaulting to {caption_column}")
+    else:
+        caption_column = args.caption_column
+        if caption_column not in column_names:
+            raise ValueError(
+                f"`--caption_column` value '{args.caption_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
+            )
+
+    if args.conditioning_image_column is None:
+        conditioning_image_column = column_names[2]
+        logger.info(f"conditioning image column defaulting to {caption_column}")
+    else:
+        conditioning_image_column = args.conditioning_image_column
+        if conditioning_image_column not in column_names:
+            raise ValueError(
+                f"`--conditioning_image_column` value '{args.conditioning_image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
+            )
+
+    def tokenize_captions(examples, is_train=True):
+        captions = []
+        for caption in examples[caption_column]:
+            if random.random() < args.proportion_empty_prompts:
+                captions.append("")
+            elif isinstance(caption, str):
+                captions.append(caption)
+            elif isinstance(caption, (list, np.ndarray)):
+                # take a random caption if there are multiple
+                captions.append(random.choice(caption) if is_train else caption[0])
+            else:
+                raise ValueError(
+                    f"Caption column `{caption_column}` should contain either strings or lists of strings."
+                )
+        inputs = tokenizer(
+            captions, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
+        )
+        return inputs.input_ids
+
+    image_transforms = transforms.Compose(
+        [
+            transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
+            transforms.CenterCrop(args.resolution),
+            transforms.ToTensor(),
+            transforms.Normalize([0.5], [0.5]),
+        ]
+    )
+
+    conditioning_image_transforms = transforms.Compose(
+        [
+            transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
+            transforms.CenterCrop(args.resolution),
+            transforms.ToTensor(),
+        ]
+    )
+
+    def preprocess_train(examples):
+        images = [image.convert("RGB") for image in examples[image_column]]
+        images = [image_transforms(image) for image in images]
+
+        conditioning_images = [image.convert("RGB") for image in examples[conditioning_image_column]]
+        conditioning_images = [conditioning_image_transforms(image) for image in conditioning_images]
+
+        examples["pixel_values"] = images
+        examples["conditioning_pixel_values"] = conditioning_images
+        examples["input_ids"] = tokenize_captions(examples)
+
+        return examples
+
+    if jax.process_index() == 0:
+        if args.max_train_samples is not None:
+            if args.streaming:
+                dataset["train"] = dataset["train"].shuffle(seed=args.seed).take(args.max_train_samples)
+            else:
+                dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
+        # Set the training transforms
+        if args.streaming:
+            train_dataset = dataset["train"].map(
+                preprocess_train,
+                batched=True,
+                batch_size=batch_size,
+                remove_columns=list(dataset["train"].features.keys()),
+            )
+        else:
+            train_dataset = dataset["train"].with_transform(preprocess_train)
+
+    return train_dataset
+
+
+def collate_fn(examples):
+    pixel_values = torch.stack([example["pixel_values"] for example in examples])
+    pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
+
+    conditioning_pixel_values = torch.stack([example["conditioning_pixel_values"] for example in examples])
+    conditioning_pixel_values = conditioning_pixel_values.to(memory_format=torch.contiguous_format).float()
+
+    input_ids = torch.stack([example["input_ids"] for example in examples])
+
+    batch = {
+        "pixel_values": pixel_values,
+        "conditioning_pixel_values": conditioning_pixel_values,
+        "input_ids": input_ids,
+    }
+    batch = {k: v.numpy() for k, v in batch.items()}
+    return batch
+
+
+def get_params_to_save(params):
+    return jax.device_get(jax.tree_util.tree_map(lambda x: x[0], params))
+
+
+def main():
+    args = parse_args()
+
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        level=logging.INFO,
+    )
+    # Setup logging, we only want one process per machine to log things on the screen.
+    logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
+    if jax.process_index() == 0:
+        transformers.utils.logging.set_verbosity_info()
+    else:
+        transformers.utils.logging.set_verbosity_error()
+
+    # wandb init
+    if jax.process_index() == 0 and args.report_to == "wandb":
+        wandb.init(
+            entity=args.wandb_entity,
+            project=args.tracker_project_name,
+            job_type="train",
+            config=args,
+        )
+
+    if args.seed is not None:
+        set_seed(args.seed)
+
+    rng = jax.random.PRNGKey(0)
+
+    # Handle the repository creation
+    if jax.process_index() == 0:
+        if args.output_dir is not None:
+            os.makedirs(args.output_dir, exist_ok=True)
+
+        if args.push_to_hub:
+            repo_id = create_repo(
+                repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
+            ).repo_id
+
+    # Load the tokenizer and add the placeholder token as a additional special token
+    if args.tokenizer_name:
+        tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
+    elif args.pretrained_model_name_or_path:
+        tokenizer = CLIPTokenizer.from_pretrained(
+            args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision
+        )
+    else:
+        raise NotImplementedError("No tokenizer specified!")
+
+    # Get the datasets: you can either provide your own training and evaluation files (see below)
+    total_train_batch_size = args.train_batch_size * jax.local_device_count() * args.gradient_accumulation_steps
+    train_dataset = make_train_dataset(args, tokenizer, batch_size=total_train_batch_size)
+
+    train_dataloader = torch.utils.data.DataLoader(
+        train_dataset,
+        shuffle=not args.streaming,
+        collate_fn=collate_fn,
+        batch_size=total_train_batch_size,
+        num_workers=args.dataloader_num_workers,
+        drop_last=True,
+    )
+
+    weight_dtype = jnp.float32
+    if args.mixed_precision == "fp16":
+        weight_dtype = jnp.float16
+    elif args.mixed_precision == "bf16":
+        weight_dtype = jnp.bfloat16
+
+    # Load models and create wrapper for stable diffusion
+    text_encoder = FlaxCLIPTextModel.from_pretrained(
+        args.pretrained_model_name_or_path,
+        subfolder="text_encoder",
+        dtype=weight_dtype,
+        revision=args.revision,
+        from_pt=args.from_pt,
+    )
+    vae, vae_params = FlaxAutoencoderKL.from_pretrained(
+        args.pretrained_model_name_or_path,
+        revision=args.revision,
+        subfolder="vae",
+        dtype=weight_dtype,
+        from_pt=args.from_pt,
+    )
+    unet, unet_params = FlaxUNet2DConditionModel.from_pretrained(
+        args.pretrained_model_name_or_path,
+        subfolder="unet",
+        dtype=weight_dtype,
+        revision=args.revision,
+        from_pt=args.from_pt,
+    )
+
+    if args.controlnet_model_name_or_path:
+        logger.info("Loading existing controlnet weights")
+        controlnet, controlnet_params = FlaxControlNetModel.from_pretrained(
+            args.controlnet_model_name_or_path,
+            revision=args.controlnet_revision,
+            from_pt=args.controlnet_from_pt,
+            dtype=jnp.float32,
+        )
+    else:
+        logger.info("Initializing controlnet weights from unet")
+        rng, rng_params = jax.random.split(rng)
+
+        controlnet = FlaxControlNetModel(
+            in_channels=unet.config.in_channels,
+            down_block_types=unet.config.down_block_types,
+            only_cross_attention=unet.config.only_cross_attention,
+            block_out_channels=unet.config.block_out_channels,
+            layers_per_block=unet.config.layers_per_block,
+            attention_head_dim=unet.config.attention_head_dim,
+            cross_attention_dim=unet.config.cross_attention_dim,
+            use_linear_projection=unet.config.use_linear_projection,
+            flip_sin_to_cos=unet.config.flip_sin_to_cos,
+            freq_shift=unet.config.freq_shift,
+        )
+        controlnet_params = controlnet.init_weights(rng=rng_params)
+        controlnet_params = unfreeze(controlnet_params)
+        for key in [
+            "conv_in",
+            "time_embedding",
+            "down_blocks_0",
+            "down_blocks_1",
+            "down_blocks_2",
+            "down_blocks_3",
+            "mid_block",
+        ]:
+            controlnet_params[key] = unet_params[key]
+
+    pipeline, pipeline_params = FlaxStableDiffusionControlNetPipeline.from_pretrained(
+        args.pretrained_model_name_or_path,
+        tokenizer=tokenizer,
+        controlnet=controlnet,
+        safety_checker=None,
+        dtype=weight_dtype,
+        revision=args.revision,
+        from_pt=args.from_pt,
+    )
+    pipeline_params = jax_utils.replicate(pipeline_params)
+
+    # Optimization
+    if args.scale_lr:
+        args.learning_rate = args.learning_rate * total_train_batch_size
+
+    constant_scheduler = optax.constant_schedule(args.learning_rate)
+
+    adamw = optax.adamw(
+        learning_rate=constant_scheduler,
+        b1=args.adam_beta1,
+        b2=args.adam_beta2,
+        eps=args.adam_epsilon,
+        weight_decay=args.adam_weight_decay,
+    )
+
+    optimizer = optax.chain(
+        optax.clip_by_global_norm(args.max_grad_norm),
+        adamw,
+    )
+
+    state = train_state.TrainState.create(apply_fn=controlnet.__call__, params=controlnet_params, tx=optimizer)
+
+    noise_scheduler, noise_scheduler_state = FlaxDDPMScheduler.from_pretrained(
+        args.pretrained_model_name_or_path, subfolder="scheduler"
+    )
+
+    # Initialize our training
+    validation_rng, train_rngs = jax.random.split(rng)
+    train_rngs = jax.random.split(train_rngs, jax.local_device_count())
+
+    def compute_snr(timesteps):
+        """
+        Computes SNR as per https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L847-L849
+        """
+        alphas_cumprod = noise_scheduler_state.common.alphas_cumprod
+        sqrt_alphas_cumprod = alphas_cumprod**0.5
+        sqrt_one_minus_alphas_cumprod = (1.0 - alphas_cumprod) ** 0.5
+
+        alpha = sqrt_alphas_cumprod[timesteps]
+        sigma = sqrt_one_minus_alphas_cumprod[timesteps]
+        # Compute SNR.
+        snr = (alpha / sigma) ** 2
+        return snr
+
+    def train_step(state, unet_params, text_encoder_params, vae_params, batch, train_rng):
+        # reshape batch, add grad_step_dim if gradient_accumulation_steps > 1
+        if args.gradient_accumulation_steps > 1:
+            grad_steps = args.gradient_accumulation_steps
+            batch = jax.tree_map(lambda x: x.reshape((grad_steps, x.shape[0] // grad_steps) + x.shape[1:]), batch)
+
+        def compute_loss(params, minibatch, sample_rng):
+            # Convert images to latent space
+            vae_outputs = vae.apply(
+                {"params": vae_params}, minibatch["pixel_values"], deterministic=True, method=vae.encode
+            )
+            latents = vae_outputs.latent_dist.sample(sample_rng)
+            # (NHWC) -> (NCHW)
+            latents = jnp.transpose(latents, (0, 3, 1, 2))
+            latents = latents * vae.config.scaling_factor
+
+            # Sample noise that we'll add to the latents
+            noise_rng, timestep_rng = jax.random.split(sample_rng)
+            noise = jax.random.normal(noise_rng, latents.shape)
+            # Sample a random timestep for each image
+            bsz = latents.shape[0]
+            timesteps = jax.random.randint(
+                timestep_rng,
+                (bsz,),
+                0,
+                noise_scheduler.config.num_train_timesteps,
+            )
+
+            # Add noise to the latents according to the noise magnitude at each timestep
+            # (this is the forward diffusion process)
+            noisy_latents = noise_scheduler.add_noise(noise_scheduler_state, latents, noise, timesteps)
+
+            # Get the text embedding for conditioning
+            encoder_hidden_states = text_encoder(
+                minibatch["input_ids"],
+                params=text_encoder_params,
+                train=False,
+            )[0]
+
+            controlnet_cond = minibatch["conditioning_pixel_values"]
+
+            # Predict the noise residual and compute loss
+            down_block_res_samples, mid_block_res_sample = controlnet.apply(
+                {"params": params},
+                noisy_latents,
+                timesteps,
+                encoder_hidden_states,
+                controlnet_cond,
+                train=True,
+                return_dict=False,
+            )
+
+            model_pred = unet.apply(
+                {"params": unet_params},
+                noisy_latents,
+                timesteps,
+                encoder_hidden_states,
+                down_block_additional_residuals=down_block_res_samples,
+                mid_block_additional_residual=mid_block_res_sample,
+            ).sample
+
+            # Get the target for loss depending on the prediction type
+            if noise_scheduler.config.prediction_type == "epsilon":
+                target = noise
+            elif noise_scheduler.config.prediction_type == "v_prediction":
+                target = noise_scheduler.get_velocity(noise_scheduler_state, latents, noise, timesteps)
+            else:
+                raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
+
+            loss = (target - model_pred) ** 2
+
+            if args.snr_gamma is not None:
+                snr = jnp.array(compute_snr(timesteps))
+                snr_loss_weights = jnp.where(snr < args.snr_gamma, snr, jnp.ones_like(snr) * args.snr_gamma) / snr
+                loss = loss * snr_loss_weights
+
+            loss = loss.mean()
+
+            return loss
+
+        grad_fn = jax.value_and_grad(compute_loss)
+
+        # get a minibatch (one gradient accumulation slice)
+        def get_minibatch(batch, grad_idx):
+            return jax.tree_util.tree_map(
+                lambda x: jax.lax.dynamic_index_in_dim(x, grad_idx, keepdims=False),
+                batch,
+            )
+
+        def loss_and_grad(grad_idx, train_rng):
+            # create minibatch for the grad step
+            minibatch = get_minibatch(batch, grad_idx) if grad_idx is not None else batch
+            sample_rng, train_rng = jax.random.split(train_rng, 2)
+            loss, grad = grad_fn(state.params, minibatch, sample_rng)
+            return loss, grad, train_rng
+
+        if args.gradient_accumulation_steps == 1:
+            loss, grad, new_train_rng = loss_and_grad(None, train_rng)
+        else:
+            init_loss_grad_rng = (
+                0.0,  # initial value for cumul_loss
+                jax.tree_map(jnp.zeros_like, state.params),  # initial value for cumul_grad
+                train_rng,  # initial value for train_rng
+            )
+
+            def cumul_grad_step(grad_idx, loss_grad_rng):
+                cumul_loss, cumul_grad, train_rng = loss_grad_rng
+                loss, grad, new_train_rng = loss_and_grad(grad_idx, train_rng)
+                cumul_loss, cumul_grad = jax.tree_map(jnp.add, (cumul_loss, cumul_grad), (loss, grad))
+                return cumul_loss, cumul_grad, new_train_rng
+
+            loss, grad, new_train_rng = jax.lax.fori_loop(
+                0,
+                args.gradient_accumulation_steps,
+                cumul_grad_step,
+                init_loss_grad_rng,
+            )
+            loss, grad = jax.tree_map(lambda x: x / args.gradient_accumulation_steps, (loss, grad))
+
+        grad = jax.lax.pmean(grad, "batch")
+
+        new_state = state.apply_gradients(grads=grad)
+
+        metrics = {"loss": loss}
+        metrics = jax.lax.pmean(metrics, axis_name="batch")
+
+        def l2(xs):
+            return jnp.sqrt(sum([jnp.vdot(x, x) for x in jax.tree_util.tree_leaves(xs)]))
+
+        metrics["l2_grads"] = l2(jax.tree_util.tree_leaves(grad))
+
+        return new_state, metrics, new_train_rng
+
+    # Create parallel version of the train step
+    p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,))
+
+    # Replicate the train state on each device
+    state = jax_utils.replicate(state)
+    unet_params = jax_utils.replicate(unet_params)
+    text_encoder_params = jax_utils.replicate(text_encoder.params)
+    vae_params = jax_utils.replicate(vae_params)
+
+    # Train!
+    if args.streaming:
+        dataset_length = args.max_train_samples
+    else:
+        dataset_length = len(train_dataloader)
+    num_update_steps_per_epoch = math.ceil(dataset_length / args.gradient_accumulation_steps)
+
+    # Scheduler and math around the number of training steps.
+    if args.max_train_steps is None:
+        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
+
+    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
+
+    logger.info("***** Running training *****")
+    logger.info(f"  Num examples = {args.max_train_samples if args.streaming else len(train_dataset)}")
+    logger.info(f"  Num Epochs = {args.num_train_epochs}")
+    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
+    logger.info(f"  Total train batch size (w. parallel & distributed) = {total_train_batch_size}")
+    logger.info(f"  Total optimization steps = {args.num_train_epochs * num_update_steps_per_epoch}")
+
+    if jax.process_index() == 0 and args.report_to == "wandb":
+        wandb.define_metric("*", step_metric="train/step")
+        wandb.define_metric("train/step", step_metric="walltime")
+        wandb.config.update(
+            {
+                "num_train_examples": args.max_train_samples if args.streaming else len(train_dataset),
+                "total_train_batch_size": total_train_batch_size,
+                "total_optimization_step": args.num_train_epochs * num_update_steps_per_epoch,
+                "num_devices": jax.device_count(),
+                "controlnet_params": sum(np.prod(x.shape) for x in jax.tree_util.tree_leaves(state.params)),
+            }
+        )
+
+    global_step = step0 = 0
+    epochs = tqdm(
+        range(args.num_train_epochs),
+        desc="Epoch ... ",
+        position=0,
+        disable=jax.process_index() > 0,
+    )
+    if args.profile_memory:
+        jax.profiler.save_device_memory_profile(os.path.join(args.output_dir, "memory_initial.prof"))
+    t00 = t0 = time.monotonic()
+    for epoch in epochs:
+        # ======================== Training ================================
+
+        train_metrics = []
+        train_metric = None
+
+        steps_per_epoch = (
+            args.max_train_samples // total_train_batch_size
+            if args.streaming or args.max_train_samples
+            else len(train_dataset) // total_train_batch_size
+        )
+        train_step_progress_bar = tqdm(
+            total=steps_per_epoch,
+            desc="Training...",
+            position=1,
+            leave=False,
+            disable=jax.process_index() > 0,
+        )
+        # train
+        for batch in train_dataloader:
+            if args.profile_steps and global_step == 1:
+                train_metric["loss"].block_until_ready()
+                jax.profiler.start_trace(args.output_dir)
+            if args.profile_steps and global_step == 1 + args.profile_steps:
+                train_metric["loss"].block_until_ready()
+                jax.profiler.stop_trace()
+
+            batch = shard(batch)
+            with jax.profiler.StepTraceAnnotation("train", step_num=global_step):
+                state, train_metric, train_rngs = p_train_step(
+                    state, unet_params, text_encoder_params, vae_params, batch, train_rngs
+                )
+            train_metrics.append(train_metric)
+
+            train_step_progress_bar.update(1)
+
+            global_step += 1
+            if global_step >= args.max_train_steps:
+                break
+
+            if (
+                args.validation_prompt is not None
+                and global_step % args.validation_steps == 0
+                and jax.process_index() == 0
+            ):
+                _ = log_validation(pipeline, pipeline_params, state.params, tokenizer, args, validation_rng, weight_dtype)
+
+            if global_step % args.logging_steps == 0 and jax.process_index() == 0:
+                if args.report_to == "wandb":
+                    train_metrics = jax_utils.unreplicate(train_metrics)
+                    train_metrics = jax.tree_util.tree_map(lambda *m: jnp.array(m).mean(), *train_metrics)
+                    wandb.log(
+                        {
+                            "walltime": time.monotonic() - t00,
+                            "train/step": global_step,
+                            "train/epoch": global_step / dataset_length,
+                            "train/steps_per_sec": (global_step - step0) / (time.monotonic() - t0),
+                            **{f"train/{k}": v for k, v in train_metrics.items()},
+                        }
+                    )
+                t0, step0 = time.monotonic(), global_step
+                train_metrics = []
+            if global_step % args.checkpointing_steps == 0 and jax.process_index() == 0:
+                controlnet.save_pretrained(
+                    f"{args.output_dir}/{global_step}",
+                    params=get_params_to_save(state.params),
+                )
+
+        train_metric = jax_utils.unreplicate(train_metric)
+        train_step_progress_bar.close()
+        epochs.write(f"Epoch... ({epoch + 1}/{args.num_train_epochs} | Loss: {train_metric['loss']})")
+
+    # Final validation & store model.
+    if jax.process_index() == 0:
+        if args.validation_prompt is not None:
+            if args.profile_validation:
+                jax.profiler.start_trace(args.output_dir)
+            image_logs = log_validation(pipeline, pipeline_params, state.params, tokenizer, args, validation_rng, weight_dtype)
+            if args.profile_validation:
+                jax.profiler.stop_trace()
+        else:
+            image_logs = None
+
+        controlnet.save_pretrained(
+            args.output_dir,
+            params=get_params_to_save(state.params),
+        )
+
+        if args.push_to_hub:
+            save_model_card(
+                repo_id,
+                image_logs=image_logs,
+                base_model=args.pretrained_model_name_or_path,
+                repo_folder=args.output_dir,
+            )
+            upload_folder(
+                repo_id=repo_id,
+                folder_path=args.output_dir,
+                commit_message="End of training",
+                ignore_patterns=["step_*", "epoch_*"],
+            )
+
+    if args.profile_memory:
+        jax.profiler.save_device_memory_profile(os.path.join(args.output_dir, "memory_final.prof"))
+    logger.info("Finished training.")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/keras-dreambooth-sprint/Dreambooth_on_Hub.ipynb b/keras-dreambooth-sprint/Dreambooth_on_Hub.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..81ecad92be009b319d8525808cf92c04fad6e4c7
--- /dev/null
+++ b/keras-dreambooth-sprint/Dreambooth_on_Hub.ipynb
@@ -0,0 +1,6884 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Introduction\n",
+        "\n",
+        "In this example, we'll implement DreamBooth, a fine-tuning technique to teach new visual concepts to text-conditioned Diffusion models with just 3 - 5 images. DreamBooth was proposed in [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://arxiv.org/abs/2208.12242) by Ruiz et al. \n",
+        "\n",
+        "DreamBooth, in a sense, is similar to the [traditional way of fine-tuning a text-conditioned Diffusion model except](https://keras.io/examples/generative/finetune_stable_diffusion/) for a few gotchas. This example assumes that you have basic familiarity with Diffusion models and how to fine-tune them. Here are some reference examples that might help you to get familiarized quickly:\n",
+        "\n",
+        "* [High-performance image generation using Stable Diffusion in KerasCV](https://keras.io/guides/keras_cv/generate_images_with_stable_diffusion/)\n",
+        "* [Teach StableDiffusion new concepts via Textual Inversion](https://keras.io/examples/generative/fine_tune_via_textual_inversion/)\n",
+        "* [Fine-tuning Stable Diffusion](https://keras.io/examples/generative/finetune_stable_diffusion/)\n",
+        "\n",
+        "First, let's install the latest versions of KerasCV and TensorFlow. \n"
+      ],
+      "metadata": {
+        "id": "CxCo2Sx5oYP5"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "GrRLwS-IPzGi",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "2b332694-23db-4ae6-da2f-a80e93ad4cb8"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m615.8/615.8 KB\u001b[0m \u001b[31m28.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m588.3/588.3 MB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m439.2/439.2 KB\u001b[0m \u001b[31m43.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m82.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.0/6.0 MB\u001b[0m \u001b[31m121.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install -q -U keras_cv\n",
+        "!pip install -q -U tensorflow "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "If you're running the code, please ensure you're using a GPU with at least 24 GBs of VRAM. "
+      ],
+      "metadata": {
+        "id": "bEI1BW12qKil"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Initial imports"
+      ],
+      "metadata": {
+        "id": "GhufITnU5N3D"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "zt50YWhpP3QP",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "c295c635-d94d-4b4d-b9aa-043865dc0ec9"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "You do not have Waymo Open Dataset installed, so KerasCV Waymo metrics are not available.\n"
+          ]
+        }
+      ],
+      "source": [
+        "import tensorflow as tf \n",
+        "\n",
+        "from keras_cv.models.stable_diffusion.clip_tokenizer import SimpleTokenizer\n",
+        "from keras_cv.models.stable_diffusion.diffusion_model import DiffusionModel\n",
+        "from keras_cv.models.stable_diffusion.image_encoder import ImageEncoder\n",
+        "from keras_cv.models.stable_diffusion.noise_scheduler import NoiseScheduler\n",
+        "from keras_cv.models.stable_diffusion.text_encoder import TextEncoder"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Usage od DreamBooth\n",
+        "\n",
+        "... is very versatile. By teaching Stable Diffusion about your favorite visual concepts, you can \n",
+        "\n",
+        "* Recontextualize objects in interesting ways:\n",
+        "\n",
+        "  ![](https://i.imgur.com/4Da9ozw.png)\n",
+        "\n",
+        "* Generate artistic renderings of the underlying visual concept: \n",
+        "\n",
+        "  ![](https://i.imgur.com/nI2N8bI.png)\n",
+        "\n",
+        "\n",
+        "And many other applications. We welcome you to check out the original DreamBooth paper in [this regard](https://arxiv.org/abs/2208.12242). "
+      ],
+      "metadata": {
+        "id": "JZjE4Ri7jhVW"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Download the instance and class images\n",
+        "\n",
+        "DreamBooth uses a technique called \"prior preservation\" to meaningfully guide the training procedure such that the fine-tuned models can still preserve some of the prior semantics of the visual concept you're introducing. To know more about the idea of \"prior preservation\" refer to [this document](https://dreambooth.github.io/).\n",
+        "\n",
+        "Here, we need to introduce a few key terms specific to DreamBooth:\n",
+        "\n",
+        "* **Unique class**: Examples include \"dog\", \"person\", etc. In this example, we use \"dog\". \n",
+        "* **Unique identifier**: A unique identifier that is prepended to the unique class while forming the \"instance prompts\". In this example, we use \"sks\" as this unique identifier. \n",
+        "* **Instance prompt**: Denotes a prompt that best describes the \"instance images\". An example prompt could be - \"f\"a photo of {unique_id} {unique_class}\". So, for our example, this becomes - \"a photo  of sks dog\".\n",
+        "* **Class prompt**: Denotes a prompt without the unique identifier. This prompt is used for generating \"class images\" for prior preservation. For our example, this prompt is - \"a photo of dog\".\n",
+        "* **Instance images**: Denote the images that represent the visual concept you're trying to teach aka the \"instance prompt\". This number is typically just 3 - 5. We typically gather these images ourselves. \n",
+        "* **Class images**: Denote the images generated using the \"class prompt\" for using prior preservation in DreamBooth training. We leverage the pre-trained model before fine-tuning it to generate these class images. Typically, 200 - 300 class images are enough.\n",
+        "\n",
+        "In code, this generation process looks quite simply:\n",
+        "\n",
+        "```py\n",
+        "from tqdm import tqdm\n",
+        "import numpy as np \n",
+        "import hashlib\n",
+        "import keras_cv\n",
+        "import PIL \n",
+        "import os\n",
+        "\n",
+        "class_images_dir = \"class-images\"\n",
+        "os.makedirs(class_images_dir, exist_ok=True)\n",
+        "\n",
+        "model = keras_cv.models.StableDiffusion(img_width=512, img_height=512, jit_compile=True)\n",
+        "\n",
+        "class_prompt = \"a photo of dog\"\n",
+        "num_imgs_to_generate = 200 \n",
+        "for i in tqdm(range(num_imgs_to_generate)):\n",
+        "    images = model.text_to_image(\n",
+        "        class_prompt,\n",
+        "        batch_size=3,\n",
+        "    )\n",
+        "    idx = np.random.choice(len(images))\n",
+        "    selected_image = PIL.Image.fromarray(images[idx])\n",
+        "    \n",
+        "    hash_image = hashlib.sha1(selected_image.tobytes()).hexdigest()\n",
+        "    image_filename = os.path.join(class_images_dir, f\"{hash_image}.jpg\")\n",
+        "    selected_image.save(image_filename)\n",
+        "```\n",
+        "\n",
+        "To keep the runtime of this example short, the authors of this example have gone ahead and generated some class images using [this notebook](https://colab.research.google.com/gist/sayakpaul/6b5de345d29cf5860f84b6d04d958692/generate_class_priors.ipynb). \n",
+        "\n",
+        "**Note** that prior preservation is an optional technique used in DreamBooth, but it almost always helps in improving the quality of the generated images. "
+      ],
+      "metadata": {
+        "id": "Lj12_v-W5P29"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "dTSJSBHva3FE",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "4f99da07-475f-44de-9fc9-3b5d6b03b2f9"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading data from https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/instance-images.tar.gz\n",
+            "5556967/5556967 [==============================] - 1s 0us/step\n",
+            "Downloading data from https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/class-images.tar.gz\n",
+            "9093120/9093120 [==============================] - 1s 0us/step\n"
+          ]
+        }
+      ],
+      "source": [
+        "instance_images_root = tf.keras.utils.get_file(\n",
+        "    origin=\"https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/instance-images.tar.gz\",\n",
+        "    untar=True\n",
+        ")\n",
+        "class_images_root = tf.keras.utils.get_file(\n",
+        "    origin=\"https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/class-images.tar.gz\",\n",
+        "    untar=True\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Visualize images\n",
+        "\n",
+        "First, let's load the image paths. "
+      ],
+      "metadata": {
+        "id": "LO38MVgG5VgD"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "LwL4KcZPSbpP",
+        "outputId": "b186c062-7168-416e-afe8-4dc7c0e64f2e"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(['/root/.keras/datasets/instance-images/alvan-nee-Id1DBHv4fbg-unsplash.jpeg',\n",
+              "  '/root/.keras/datasets/instance-images/alvan-nee-eoqnr8ikwFE-unsplash.jpeg',\n",
+              "  '/root/.keras/datasets/instance-images/alvan-nee-bQaAJCbNq3g-unsplash.jpeg',\n",
+              "  '/root/.keras/datasets/instance-images/alvan-nee-brFsZ7qszSY-unsplash.jpeg',\n",
+              "  '/root/.keras/datasets/instance-images/alvan-nee-9M0tSjb-cpA-unsplash.jpeg'],\n",
+              " ['/root/.keras/datasets/class-images/9a2292f120100d6890d176dd24e80c91e947dce8.jpg',\n",
+              "  '/root/.keras/datasets/class-images/fda86efe881d46063528e5d04cb4129ac3e0a8e9.jpg',\n",
+              "  '/root/.keras/datasets/class-images/b2e9dc379f60c198bac758c9b7d3101fb8b585df.jpg',\n",
+              "  '/root/.keras/datasets/class-images/95f289f89787fe6bb894519fa431e3ed31814e19.jpg',\n",
+              "  '/root/.keras/datasets/class-images/17b9ea0913cabfcf11cf41363de0a21c36f2e170.jpg'])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ],
+      "source": [
+        "from imutils import paths\n",
+        "\n",
+        "instance_image_paths = list(paths.list_images(instance_images_root))\n",
+        "class_image_paths = list(paths.list_images(class_images_root))\n",
+        "\n",
+        "instance_image_paths, class_image_paths[:5]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Then we load the images from the paths. "
+      ],
+      "metadata": {
+        "id": "gUA2XZlRuQIX"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "1jo5kgIaTAkP"
+      },
+      "outputs": [],
+      "source": [
+        "from PIL import Image\n",
+        "import numpy as np\n",
+        "\n",
+        "def load_images(image_paths):\n",
+        "    images = [] \n",
+        "    for path in image_paths:\n",
+        "        image = Image.open(path)\n",
+        "        images.append(np.array(image))\n",
+        "    return images"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "And then we make use a utility function to plot the loaded images. "
+      ],
+      "metadata": {
+        "id": "Bm0FB93kuUnl"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_mavOcLhTKRP"
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "def plot_images(images, title=None):\n",
+        "    plt.figure(figsize=(20, 20))\n",
+        "    for i in range(len(images)):\n",
+        "        ax = plt.subplot(1, len(images), i + 1)\n",
+        "        if title is not None:\n",
+        "            plt.title(title)\n",
+        "        plt.imshow(images[i])\n",
+        "        plt.axis(\"off\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Instance images**:"
+      ],
+      "metadata": {
+        "id": "NjD0kS-ttLnA"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plot_images(load_images(instance_image_paths[:5]))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 239
+        },
+        "id": "V-fwNGDPtNFD",
+        "outputId": "625a3a8b-9d66-4bef-e59d-c7f2a6cd2f45"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1440x1440 with 5 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Class images**:"
+      ],
+      "metadata": {
+        "id": "ZryM4IWmtXO6"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plot_images(load_images(class_image_paths[:5]))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 223
+        },
+        "id": "sVtz4uOAtZhq",
+        "outputId": "ec063430-646e-4df0-e0c5-daf73b72ff12"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1440x1440 with 5 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Prepare datasets\n",
+        "\n",
+        "Dataset preparation includes two stages: (1): preparing the captions, (2) processing the images."
+      ],
+      "metadata": {
+        "id": "VLkqpqzZ5Yk-"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Prepare the captions"
+      ],
+      "metadata": {
+        "id": "gAytBq0f5egN"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Since we're using prior preservation, we need to match the number\n",
+        "# of instance images we're using. We just repeat the instance image paths\n",
+        "# to do so. \n",
+        "new_instance_image_paths = []\n",
+        "for index in range(len(class_image_paths)):\n",
+        "    instance_image = instance_image_paths[index % len(instance_image_paths)]\n",
+        "    new_instance_image_paths.append(instance_image)"
+      ],
+      "metadata": {
+        "id": "oBouQk5IqfAH"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "v4uExJ9uVV1G"
+      },
+      "outputs": [],
+      "source": [
+        "# We just repeat the prompts / captions per images. \n",
+        "unique_id = \"sks\"\n",
+        "class_label = \"dog\"\n",
+        "\n",
+        "instance_prompt = f\"a photo of {unique_id} {class_label}\" \n",
+        "instance_prompts = [instance_prompt] * len(new_instance_image_paths)\n",
+        "\n",
+        "class_prompt = f\"a photo of {class_label}\"\n",
+        "class_prompts = [class_prompt] * len(class_image_paths)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Next, we embed the prompts to save some compute.  "
+      ],
+      "metadata": {
+        "id": "7bOreYO5vELs"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "kOO0HZCEe_A4",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "46a24ee6-8c29-456a-bc53-400fbe0a6904"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading data from https://github.com/openai/CLIP/blob/main/clip/bpe_simple_vocab_16e6.txt.gz?raw=true\n",
+            "1356917/1356917 [==============================] - 0s 0us/step\n",
+            "Downloading data from https://huggingface.co/fchollet/stable-diffusion/resolve/main/kcv_encoder.h5\n",
+            "492466864/492466864 [==============================] - 3s 0us/step\n"
+          ]
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "import itertools\n",
+        "\n",
+        "# The padding token and maximum prompt length are specific to the text encoder.\n",
+        "# If you're using a different text encoder be sure to change them accordingly.\n",
+        "padding_token = 49407\n",
+        "max_prompt_length = 77\n",
+        "\n",
+        "# Load the tokenizer.\n",
+        "tokenizer = SimpleTokenizer()\n",
+        "\n",
+        "# Method to tokenize and pad the tokens.\n",
+        "def process_text(caption):\n",
+        "    tokens = tokenizer.encode(caption)\n",
+        "    tokens = tokens + [padding_token] * (max_prompt_length - len(tokens))\n",
+        "    return np.array(tokens)\n",
+        "\n",
+        "\n",
+        "# Collate the tokenized captions into an array.\n",
+        "tokenized_texts = np.empty((len(instance_prompts) + len(class_prompts), max_prompt_length))\n",
+        "\n",
+        "for i, caption in enumerate(itertools.chain(instance_prompts, class_prompts)):\n",
+        "    tokenized_texts[i] = process_text(caption)\n",
+        "\n",
+        "\n",
+        "# We also pre-compute the text embeddings to save some memory during training. \n",
+        "POS_IDS = tf.convert_to_tensor([list(range(max_prompt_length))], dtype=tf.int32)\n",
+        "text_encoder = TextEncoder(max_prompt_length)\n",
+        "\n",
+        "gpus = tf.config.list_logical_devices(\"GPU\")\n",
+        "\n",
+        "# Ensure the computation takes place on a GPU.\n",
+        "with tf.device(gpus[0].name):\n",
+        "    embedded_text = text_encoder(\n",
+        "        [tf.convert_to_tensor(tokenized_texts), POS_IDS], training=False\n",
+        "    ).numpy()\n",
+        "\n",
+        "# To ensure text_encoder doesn't occupy any GPU space.\n",
+        "del text_encoder"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Prepare the images"
+      ],
+      "metadata": {
+        "id": "IEKtiNFJ5iII"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2ylZ3svIf_RA"
+      },
+      "outputs": [],
+      "source": [
+        "import keras_cv\n",
+        "\n",
+        "resolution = 512\n",
+        "auto = tf.data.AUTOTUNE\n",
+        "\n",
+        "augmenter = keras_cv.layers.Augmenter(\n",
+        "    layers=[\n",
+        "        keras_cv.layers.CenterCrop(resolution, resolution),\n",
+        "        keras_cv.layers.RandomFlip(),\n",
+        "        tf.keras.layers.Rescaling(scale=1.0 / 127.5, offset=-1),\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "\n",
+        "def process_image(image_path, tokenized_text):\n",
+        "    image = tf.io.read_file(image_path)\n",
+        "    image = tf.io.decode_png(image, 3)\n",
+        "    image = tf.image.resize(image, (resolution, resolution))\n",
+        "    return image, tokenized_text\n",
+        "\n",
+        "\n",
+        "def apply_augmentation(image_batch, embedded_tokens):\n",
+        "    return augmenter(image_batch), embedded_tokens\n",
+        "\n",
+        "\n",
+        "def prepare_dict(instance_only=True):\n",
+        "    def fn(image_batch, embedded_tokens):\n",
+        "        if instance_only:\n",
+        "            batch_dict = {\n",
+        "                \"instance_images\": image_batch,\n",
+        "                \"instance_embedded_texts\": embedded_tokens,\n",
+        "            }\n",
+        "            return batch_dict\n",
+        "        else:\n",
+        "            batch_dict = {\n",
+        "                \"class_images\": image_batch,\n",
+        "                \"class_embedded_texts\": embedded_tokens,\n",
+        "            }\n",
+        "            return batch_dict\n",
+        "    return fn\n",
+        "\n",
+        "\n",
+        "def assemble_dataset(\n",
+        "    image_paths, embedded_texts, instance_only=True, batch_size=1\n",
+        "):  \n",
+        "    dataset = tf.data.Dataset.from_tensor_slices(\n",
+        "        (image_paths, embedded_texts)\n",
+        "    )\n",
+        "    dataset = dataset.map(process_image, num_parallel_calls=auto)\n",
+        "    dataset = dataset.shuffle(5, reshuffle_each_iteration=True)\n",
+        "    dataset = dataset.batch(batch_size)\n",
+        "    dataset = dataset.map(apply_augmentation, num_parallel_calls=auto)\n",
+        "\n",
+        "    prepare_dict_fn = prepare_dict(instance_only=instance_only)\n",
+        "    dataset = dataset.map(prepare_dict_fn, num_parallel_calls=auto)\n",
+        "    return dataset"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Assemble dataset"
+      ],
+      "metadata": {
+        "id": "5bH2Uj9t5k17"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "B95a2gpEghgA",
+        "outputId": "09d86be2-add7-45ac-b33c-b441f5084941"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
+            "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
+            "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
+            "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
+            "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
+            "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
+            "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
+            "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n"
+          ]
+        }
+      ],
+      "source": [
+        "instance_dataset = assemble_dataset(\n",
+        "    new_instance_image_paths, \n",
+        "    embedded_text[:len(new_instance_image_paths)],\n",
+        ")\n",
+        "class_dataset = assemble_dataset(\n",
+        "    class_image_paths, \n",
+        "    embedded_text[len(new_instance_image_paths):],\n",
+        "    instance_only=False\n",
+        ")\n",
+        "train_dataset = tf.data.Dataset.zip((instance_dataset, class_dataset))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Check shapes\n",
+        "\n",
+        "Now that the dataset has been prepared, let's quickly check what's inside it. "
+      ],
+      "metadata": {
+        "id": "DvhkmFMU5nLG"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sample_batch = next(iter(train_dataset))\n",
+        "print(sample_batch[0].keys(), sample_batch[1].keys())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pGZz1WXPvK0O",
+        "outputId": "e06cd69d-f1f5-432c-9158-6e2551189c44"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "dict_keys(['instance_images', 'instance_embedded_texts']) dict_keys(['class_images', 'class_embedded_texts'])\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "for k in sample_batch[0]:\n",
+        "    print(k, sample_batch[0][k].shape)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mmRT6AB-3oum",
+        "outputId": "a0bc826b-5f2a-42a7-dd60-9da2beeb6725"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "instance_images (1, 512, 512, 3)\n",
+            "instance_embedded_texts (1, 77, 768)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "for k in sample_batch[1]:\n",
+        "    print(k, sample_batch[1][k].shape)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "oDsE6csN5E1X",
+        "outputId": "6461ab2f-ee73-4e48-8170-d1799c483bc2"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "class_images (1, 512, 512, 3)\n",
+            "class_embedded_texts (1, 77, 768)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "During training, we make use of these keys to gather the images and text embeddings and concat them accordingly. "
+      ],
+      "metadata": {
+        "id": "QBImMZPhye38"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## DreamBooth training loop\n",
+        "\n",
+        "Our DreamBooth training loop is very much inspired by [this script](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth.py) provided by the Diffusers team at Hugging Face. However, there is an important difference to note. We only fine-tune the UNet (the model responsible for predicting noise) and don't fine-tune the text encoder in this example. If you're looking for an implementation that also performs the additional fine-tuning of the text encoder, refer to [this repository](https://github.com/sayakpaul/dreambooth-keras/). "
+      ],
+      "metadata": {
+        "id": "sdH2FfCy5oW9"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import tensorflow.experimental.numpy as tnp\n",
+        "\n",
+        "class DreamBoothTrainer(tf.keras.Model):\n",
+        "    # Reference:\n",
+        "    # https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth.py\n",
+        "\n",
+        "    def __init__(\n",
+        "        self,\n",
+        "        diffusion_model,\n",
+        "        vae,\n",
+        "        noise_scheduler,\n",
+        "        use_mixed_precision=False,\n",
+        "        prior_loss_weight=1.0,\n",
+        "        max_grad_norm=1.0,\n",
+        "        **kwargs\n",
+        "    ):\n",
+        "        super().__init__(**kwargs)\n",
+        "\n",
+        "        self.diffusion_model = diffusion_model\n",
+        "        self.vae = vae\n",
+        "        self.noise_scheduler = noise_scheduler\n",
+        "        self.prior_loss_weight = prior_loss_weight\n",
+        "        self.max_grad_norm = max_grad_norm\n",
+        "\n",
+        "        self.use_mixed_precision = use_mixed_precision\n",
+        "        self.vae.trainable = False\n",
+        "\n",
+        "    def train_step(self, inputs):\n",
+        "        instance_batch = inputs[0]\n",
+        "        class_batch = inputs[1]\n",
+        "\n",
+        "        instance_images = instance_batch[\"instance_images\"]\n",
+        "        instance_embedded_text = instance_batch[\"instance_embedded_texts\"]\n",
+        "        class_images = class_batch[\"class_images\"]\n",
+        "        class_embedded_text = class_batch[\"class_embedded_texts\"]\n",
+        "\n",
+        "        images = tf.concat([instance_images, class_images], 0)\n",
+        "        embedded_texts = tf.concat([instance_embedded_text, class_embedded_text], 0)\n",
+        "        batch_size = tf.shape(images)[0]\n",
+        "\n",
+        "        with tf.GradientTape() as tape:\n",
+        "            # Project image into the latent space and sample from it.\n",
+        "            latents = self.sample_from_encoder_outputs(self.vae(images, training=False))\n",
+        "            # Know more about the magic number here:\n",
+        "            # https://keras.io/examples/generative/fine_tune_via_textual_inversion/\n",
+        "            latents = latents * 0.18215\n",
+        "\n",
+        "            # Sample noise that we'll add to the latents.\n",
+        "            noise = tf.random.normal(tf.shape(latents))\n",
+        "\n",
+        "            # Sample a random timestep for each image.\n",
+        "            timesteps = tnp.random.randint(\n",
+        "                0, self.noise_scheduler.train_timesteps, (batch_size,)\n",
+        "            )\n",
+        "\n",
+        "            # Add noise to the latents according to the noise magnitude at each timestep\n",
+        "            # (this is the forward diffusion process).\n",
+        "            noisy_latents = self.noise_scheduler.add_noise(\n",
+        "                tf.cast(latents, noise.dtype), noise, timesteps\n",
+        "            )\n",
+        "\n",
+        "            # Get the target for loss depending on the prediction type\n",
+        "            # just the sampled noise for now.\n",
+        "            target = noise  # noise_schedule.predict_epsilon == True\n",
+        "\n",
+        "            # Predict the noise residual and compute loss.\n",
+        "            timestep_embedding = tf.map_fn(\n",
+        "                lambda t: self.get_timestep_embedding(t), timesteps, dtype=tf.float32\n",
+        "            )\n",
+        "            model_pred = self.diffusion_model(\n",
+        "                [noisy_latents, timestep_embedding, embedded_texts], training=True\n",
+        "            )\n",
+        "            loss = self.compute_loss(target, model_pred)\n",
+        "            if self.use_mixed_precision:\n",
+        "                loss = self.optimizer.get_scaled_loss(loss)\n",
+        "\n",
+        "        # Update parameters of the diffusion model.\n",
+        "        trainable_vars = self.diffusion_model.trainable_variables\n",
+        "        gradients = tape.gradient(loss, trainable_vars)\n",
+        "        if self.use_mixed_precision:\n",
+        "            gradients = self.optimizer.get_unscaled_gradients(gradients)\n",
+        "        gradients = [tf.clip_by_norm(g, self.max_grad_norm) for g in gradients]\n",
+        "        self.optimizer.apply_gradients(zip(gradients, trainable_vars))\n",
+        "\n",
+        "        return {m.name: m.result() for m in self.metrics}\n",
+        "\n",
+        "    def get_timestep_embedding(self, timestep, dim=320, max_period=10000):\n",
+        "        half = dim // 2\n",
+        "        log_max_preiod = tf.math.log(tf.cast(max_period, tf.float32))\n",
+        "        freqs = tf.math.exp(\n",
+        "            -log_max_preiod * tf.range(0, half, dtype=tf.float32) / half\n",
+        "        )\n",
+        "        args = tf.convert_to_tensor([timestep], dtype=tf.float32) * freqs\n",
+        "        embedding = tf.concat([tf.math.cos(args), tf.math.sin(args)], 0)\n",
+        "        return embedding\n",
+        "\n",
+        "    def sample_from_encoder_outputs(self, outputs):\n",
+        "        mean, logvar = tf.split(outputs, 2, axis=-1)\n",
+        "        logvar = tf.clip_by_value(logvar, -30.0, 20.0)\n",
+        "        std = tf.exp(0.5 * logvar)\n",
+        "        sample = tf.random.normal(tf.shape(mean), dtype=mean.dtype)\n",
+        "        return mean + std * sample\n",
+        "\n",
+        "    def compute_loss(self, target, model_pred):\n",
+        "        # Chunk the noise and model_pred into two parts and compute the loss\n",
+        "        # on each part separately.\n",
+        "        # Since the first half of the inputs has instance samples and the second half\n",
+        "        # has class samples, we do the chunking accordingly. \n",
+        "        model_pred, model_pred_prior = tf.split(model_pred, num_or_size_splits=2, axis=0)\n",
+        "        target, target_prior = tf.split(target, num_or_size_splits=2, axis=0)\n",
+        "\n",
+        "        # Compute instance loss.\n",
+        "        loss = self.compiled_loss(target, model_pred)\n",
+        "\n",
+        "        # Compute prior loss.\n",
+        "        prior_loss = self.compiled_loss(target_prior, model_pred_prior)\n",
+        "\n",
+        "        # Add the prior loss to the instance loss.\n",
+        "        loss = loss + self.prior_loss_weight * prior_loss\n",
+        "        return loss\n",
+        "\n",
+        "    def save_weights(self, filepath, overwrite=True, save_format=None, options=None):\n",
+        "        # Overriding this method will allow us to use the `ModelCheckpoint`\n",
+        "        # callback directly with this trainer class. In this case, it will\n",
+        "        # only checkpoint the `diffusion_model` since that's what we're training\n",
+        "        # during fine-tuning.\n",
+        "        self.diffusion_model.save_weights(\n",
+        "            filepath=filepath,\n",
+        "            overwrite=overwrite,\n",
+        "            save_format=save_format,\n",
+        "            options=options,\n",
+        "        )"
+      ],
+      "metadata": {
+        "id": "U0I9CJwW5Jy8"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Trainer initialization"
+      ],
+      "metadata": {
+        "id": "adwmtaCjQVTt"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Comment it if you are not using a GPU having tensor cores.\n",
+        "tf.keras.mixed_precision.set_global_policy(\"mixed_float16\")"
+      ],
+      "metadata": {
+        "id": "HaIiqSV_GeYj"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "use_mp = True # Set it to False if you're not using a GPU with tensor cores. \n",
+        "\n",
+        "image_encoder = ImageEncoder(resolution, resolution)\n",
+        "dreambooth_trainer = DreamBoothTrainer(\n",
+        "    diffusion_model=DiffusionModel(resolution, resolution, max_prompt_length),\n",
+        "    # Remove the top layer from the encoder, which cuts off the variance and only\n",
+        "    # returns the mean.\n",
+        "    vae=tf.keras.Model(\n",
+        "        image_encoder.input,\n",
+        "        image_encoder.layers[-2].output,\n",
+        "    ),\n",
+        "    noise_scheduler=NoiseScheduler(),\n",
+        "    use_mixed_precision=use_mp,\n",
+        ")\n",
+        "\n",
+        "# These hyperparameters come from this tutorial by Hugging Face:\n",
+        "# https://github.com/huggingface/diffusers/tree/main/examples/dreambooth\n",
+        "lr = 5e-6\n",
+        "beta_1, beta_2 = 0.9, 0.999\n",
+        "weight_decay = (1e-2,)\n",
+        "epsilon = 1e-08\n",
+        "\n",
+        "optimizer = tf.keras.optimizers.experimental.AdamW(\n",
+        "    learning_rate=lr,\n",
+        "    weight_decay=weight_decay,\n",
+        "    beta_1=beta_1,\n",
+        "    beta_2=beta_2,\n",
+        "    epsilon=epsilon,\n",
+        ")\n",
+        "dreambooth_trainer.compile(optimizer=optimizer, loss=\"mse\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zHqE_1jqGrb8",
+        "outputId": "4daea5fd-b7a8-4c46-9126-c30b8c355983"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.\n",
+            "Instructions for updating:\n",
+            "Lambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading data from https://huggingface.co/fchollet/stable-diffusion/resolve/main/vae_encoder.h5\n",
+            "136824240/136824240 [==============================] - 1s 0us/step\n",
+            "Downloading data from https://huggingface.co/fchollet/stable-diffusion/resolve/main/kcv_diffusion_model.h5\n",
+            "3439090152/3439090152 [==============================] - 52s 0us/step\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Train!\n",
+        "\n",
+        "We first calculate the number of epochs, we need to train for."
+      ],
+      "metadata": {
+        "id": "xtSiKL_JQXZK"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import math\n",
+        "\n",
+        "num_update_steps_per_epoch = train_dataset.cardinality()\n",
+        "max_train_steps = 800\n",
+        "epochs =  math.ceil(max_train_steps / num_update_steps_per_epoch)\n",
+        "print(f\"Training for {epochs} epochs.\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "yYsTS_pEKoSx",
+        "outputId": "4904f882-e341-4ee9-f337-95d733c4e5e6"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Training for 4 epochs.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "And then we start training!"
+      ],
+      "metadata": {
+        "id": "kEj2bIVpz3b_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ckpt_path = \"dreambooth-unet.h5\" \n",
+        "ckpt_callback = tf.keras.callbacks.ModelCheckpoint(\n",
+        "    ckpt_path,\n",
+        "    save_weights_only=True,\n",
+        "    monitor=\"loss\",\n",
+        "    mode=\"min\",\n",
+        ")\n",
+        "dreambooth_trainer.fit(train_dataset, epochs=epochs, callbacks=[ckpt_callback])"
+      ],
+      "metadata": {
+        "id": "0ojwJlqxIiuW",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "27a61451-b6d6-42d0-c0c4-a0be256d23c5"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/4\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/deprecation.py:629: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a future version.\n",
+            "Instructions for updating:\n",
+            "Use fn_output_signature instead\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Model Hosting\n",
+        "To host the model, we will use [Hugging Face Hub](https://huggingface.co/). It follows an authentication with token so we need to get our write token from [this page](https://huggingface.co/settings/tokens) and we will use `notebook_login()` to pass the token. Hosting the model using utils from Hugging Face Hub enables better versioning of models with reproducibility tools, e.g. model cards, and also easy loading without storing the model locally."
+      ],
+      "metadata": {
+        "id": "6LbXn5WdtZG7"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Initialize a new Stable Diffusion model.\n",
+        "dreambooth_model = keras_cv.models.StableDiffusion(\n",
+        "    img_width=resolution, img_height=resolution, jit_compile=True\n",
+        ")\n",
+        "dreambooth_model.diffusion_model.load_weights(ckpt_path)"
+      ],
+      "metadata": {
+        "id": "pMEiROvftaPu"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install huggingface-hub"
+      ],
+      "metadata": {
+        "id": "b6QCXRsksYnn",
+        "outputId": "2e064cb0-90fc-419f-b565-3c73c56b5392",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
+            "Collecting huggingface-hub\n",
+            "  Downloading huggingface_hub-0.12.0-py3-none-any.whl (190 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m190.3/190.3 KB\u001b[0m \u001b[31m11.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.8/dist-packages (from huggingface-hub) (4.64.1)\n",
+            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.8/dist-packages (from huggingface-hub) (4.4.0)\n",
+            "Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from huggingface-hub) (2.25.1)\n",
+            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.8/dist-packages (from huggingface-hub) (23.0)\n",
+            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.8/dist-packages (from huggingface-hub) (6.0)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from huggingface-hub) (3.9.0)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface-hub) (2022.12.7)\n",
+            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface-hub) (1.24.3)\n",
+            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface-hub) (2.10)\n",
+            "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface-hub) (4.0.0)\n",
+            "Installing collected packages: huggingface-hub\n",
+            "Successfully installed huggingface-hub-0.12.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from huggingface_hub import notebook_login\n",
+        "notebook_login()"
+      ],
+      "metadata": {
+        "id": "12r5HPn0scHT"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "To host our model, we will use `push_to_hub_keras()`. This function will save the model as `SavedModel`, create a model card that we will later edit, and push our model to Hugging Face. We will pass only the diffusion model, and provide the function with a repository ID (in this case, `merve/dreambooth_diffusion_model`). \n",
+        "During push to hub, you might get warnings. Feel free to ignore them."
+      ],
+      "metadata": {
+        "id": "YWtTiBNVtyPB"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from huggingface_hub import push_to_hub_keras\n",
+        "\n",
+        "config = dreambooth_model.diffusion_model.get_config()\n",
+        "repo_id = \"merve/dreambooth_diffusion_model\"\n",
+        "push_to_hub_keras(dreambooth_model.diffusion_model, repo_id, config=config)"
+      ],
+      "metadata": {
+        "id": "bKuHwywYsi0d",
+        "outputId": "099d6a1c-7f0e-4486-ba40-de65208924cd",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000,
+          "referenced_widgets": [
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+            "4439d72a53ab42b9980c79a4684ecac7",
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+            "093f79b9a59245a294190a0a1199a38d",
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+            "3424df223095402b9469cc11111862cc",
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+            "fdc9950a4dc64ceaaed7b9d53122b608",
+            "16e4220b90bf43568571b89a8a7d5f02",
+            "73ab8dde1b934274b98d479fbf7df107",
+            "4996209f55bb499db5f7356c7d524c11",
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+            "77116f82571a4b9da81d68018f95fa32",
+            "de2376940cd44c3a9cfe007df64f3b89",
+            "7160c7b7f90e441da2a4b8508358da6a",
+            "5cb336455a3a4355b2b1c21e53166091",
+            "d238beb883df4d918e22d01697b7ba37",
+            "19868a163e9d45009d835a7811bd572a",
+            "efcce00466c34c2d95da9a63c7dc3b2f",
+            "b376fed749394f4e840533f46776a04c",
+            "e963328f732048199e2239e6c8856352"
+          ]
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:absl:Found untraced functions such as conv2d_114_layer_call_fn, conv2d_114_layer_call_and_return_conditional_losses, _jit_compiled_convolution_op, group_normalization_85_layer_call_fn, group_normalization_85_layer_call_and_return_conditional_losses while saving (showing 5 of 1320). These functions will not be directly callable after loading.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb391eff70> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb39fcc8e0> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb39e8efd0> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb39db3160> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb39c04160> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb39ac7f70> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb3998e130> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb39925ca0> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb396983d0> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb396f69a0> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb393b91f0> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb392db910> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb391904c0> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb3915cb50> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb3906edc0> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb38efe790> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb38dc8d00> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdc4eda7d90> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb39fbc5e0> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb39f57c70> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb39ee8130> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb39e52460> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb39d9e7f0> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb39cfd550> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb39c56280> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb39bc3b20> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb39b14100> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb39a64df0> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb399d63a0> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb39a6e0d0> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb39964250> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb39950d00> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb399b55b0> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb3993eca0> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb39a31dc0> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb398f0970> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb3980e4c0> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb3982b4c0> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb39819880> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb397ee9a0> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb39749130> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb3974fd60> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb3976b9a0> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb3967c610> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb39689f70> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb39624e80> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb39610d00> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb394b8d90> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb394cebb0> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb392c4a60> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb392c4400> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb3923af70> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb3918a190> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb3923a070> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb3913ebb0> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb390ace50> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb390b5430> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb3902f490> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb38ef97c0> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb38ef4bb0> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
+            "WARNING:absl:<keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization object at 0x7fdb38e2ecd0> has the same name 'GroupNormalization' as a built-in Keras object. Consider renaming <class 'keras_cv.models.stable_diffusion.__internal__.layers.group_normalization.GroupNormalization'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "saved_model.pb:   0%|          | 0.00/19.6M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "8a8955009de6421ea3b5d48ee8e6f14d"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "keras_metadata.pb:   0%|          | 0.00/829k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "aef75430bbd147359e7f5022aaea3b83"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "variables.data-00000-of-00001:   0%|          | 0.00/3.44G [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "cea917949fc4454cb18add6ca4e3a5b5"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "fingerprint.pb:   0%|          | 0.00/54.0 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "1cec1bc6a64e475eaa1cc36b1afbc547"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Upload 4 LFS files:   0%|          | 0/4 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "4996209f55bb499db5f7356c7d524c11"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'https://huggingface.co/merve/dreambooth_diffusion_model/tree/main/'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 29
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Inference"
+      ],
+      "metadata": {
+        "id": "LXWHtVFfO0-j"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We can load the diffusion model model from the Hub using `from_pretrained_keras()` and set it as the value of the `_diffusion_model` attribute of the `StableDiffusion` model. We will then infer with the loaded model."
+      ],
+      "metadata": {
+        "id": "taUOs_74uI0F"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from huggingface_hub import from_pretrained_keras\n",
+        "\n",
+        "\n",
+        "sd_dreambooth_model = keras_cv.models.StableDiffusion(\n",
+        "    img_width=resolution, img_height=resolution, jit_compile=True, \n",
+        ")\n",
+        "loaded_diffusion_model = from_pretrained_keras(\"merve/dreambooth_diffusion_model\")\n",
+        "sd_dreambooth_model._diffusion_model = loaded_diffusion_model"
+      ],
+      "metadata": {
+        "id": "4fykoXZnsu2y",
+        "outputId": "ab72b2cd-794a-4b16-9513-dba5ad5de479",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 393,
+          "referenced_widgets": [
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+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "By using this model checkpoint, you acknowledge that its usage is subject to the terms of the CreativeML Open RAIL-M license at https://raw.githubusercontent.com/CompVis/stable-diffusion/main/LICENSE\n"
+          ]
+        },
+        {
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+        {
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+            ],
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+        {
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+            ],
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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We can now perform inference with the model."
+      ],
+      "metadata": {
+        "id": "KAx-sJ2Nuid_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Note how the unique idenitifer and the class have been used in the prompt.\n",
+        "prompt = f\"A photo of {unique_id} {class_label} in a bucket\"\n",
+        "num_imgs_to_gen = 3\n",
+        "\n",
+        "generated_img = sd_dreambooth_model.text_to_image(\n",
+        "    prompt, batch_size=num_imgs_to_gen\n",
+        ")\n",
+        "plot_images(generated_img, prompt)"
+      ],
+      "metadata": {
+        "id": "yiggE-o6s__a"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Feel free to experiment with different prompts (don't forget to add the unique identifer and the class label!) to see how the results change. We welcome you to check out our codebase and more experimental results [here](https://github.com/sayakpaul/dreambooth-keras#results). You can also refer to [this blog post](https://huggingface.co/blog/dreambooth) to get more experimentation ideas."
+      ],
+      "metadata": {
+        "id": "mF-BDocTvrBz"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Acknowledgements\n",
+        "\n",
+        "* Thanks to the [DreamBooth example script](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth.py) provided by Hugging Face which helped us a lot in getting the initial implementation ready quickly. \n",
+        "* Getting DreamBooth to work on human faces can be challenging. We have compiled some general recoomendations [here](https://github.com/sayakpaul/dreambooth-keras#notes-on-preparing-data-for-dreambooth-training-of-faces). Thanks to [Abhishek Thakur](https://no.linkedin.com/in/abhi1thakur) for helping with these. "
+      ],
+      "metadata": {
+        "id": "FZUTOBxMv_Wi"
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diff --git a/keras-dreambooth-sprint/README.md b/keras-dreambooth-sprint/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..ed99712d69ba0c1f301fd2ba5273c6b710edebc9
--- /dev/null
+++ b/keras-dreambooth-sprint/README.md
@@ -0,0 +1,185 @@
+
+![](https://huggingface.co/spaces/keras-dreambooth/README/resolve/main/dreambooth.png)
+
+**Welcome to Keras Dreambooth event!** 🤗 
+
+This document summarises all the relevant information required for the event 📋.
+
+## Introduction
+
+Dreambooth is a  fine-tuning technique to teach new visual concepts to text-conditioned Diffusion models with just 3-5 images. With Dreambooth, you could generate funny and realistic images of your dog, yourself and any concept with few images using Stable Diffusion. 
+DreamBooth was proposed in [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://arxiv.org/abs/2208.12242) by Ruiz et al.
+In this guide, we will walk you through what we will do in this event.
+
+We will be training Dreambooth models using KerasCV and building demos on them. 
+
+
+## Important Dates
+
+- Kick-Off Event: March 6th, 2023
+- Sprint start: March 7th, 2023
+- Sprint end: April 1st, 2023
+- Results: April 7th, 2023
+
+
+## Getting Started 🚀 
+
+To get started, join us in [hf.co/join/discord](http://hf.co/join/discord) and take the role #open-source, and meet us in #keras-working-group channel. 
+
+We will be hosting our demos in this organization on Hugging Face Hub: [keras-dreambooth](https://huggingface.co/keras-dreambooth), send a request to join [here](https://huggingface.co/organizations/keras-dreambooth/share/RMocthadPgpxxUDHtAesrbBzieDLgUfPmv) if you’d like to make a submission 🙂
+
+We will:
+
+1. Fine-tune Stable Diffusion on any concept we want using Dreambooth,
+2. Push the model to Hugging Face Hub,
+3. Fill the model card,
+4. Build a demo on top of the model.
+
+**Warning:** The trained models need to be in one of the 4 categories mentioned in the Submission section. Please take a look at that before training your model.
+
+**Let’s get started** 🚀 
+
+## **Model Training**
+
+You can find the notebook here and adjust it according to your own dataset 👇  
+
+[Link to notebook](https://colab.research.google.com/github/huggingface/community-events/blob/main/keras-dreambooth-sprint/Dreambooth_on_Hub.ipynb) 
+
+You can fine-tune on any concept that you want. Couple of inspirations for you: 
+
+1. Lowpoly World: This [model](https://huggingface.co/MirageML/lowpoly-world) generates lowpoly worlds 🤯🌍
+2. Future Diffusion: This [model](https://huggingface.co/nitrosocke/Future-Diffusion) generates images in futuristic sci-fi concepts 🤖
+3. Fantasy sword: This [model](https://huggingface.co/MirageML/fantasy-sword) generates swords for fantasy themed games 🧙‍♂️
+
+If you need more pointers on Dreambooth implementation with Keras, you can check out [this repository](https://github.com/sayakpaul/dreambooth-keras). 
+
+**Important**: To learn how to launch a cloud GPU instance and train with Lambda, please refer to [Compute with Lambda](./compute-with-lambda.md).
+
+## Dreambooth Diffusers Integration with KerasCV
+
+As of now, inference and deployment options of `KerasCV` are limited, which is when the `diffusers` library comes to the rescue. With only few lines of code, we can convert a `KerasCV` model into a `diffusers` one and use `diffusers`’ pipelines to perform inference. You can get more information [here](https://huggingface.co/docs/diffusers/main/en/using-diffusers/kerascv). Also check out [this Space](https://huggingface.co/spaces/sayakpaul/convert-kerascv-sd-diffusers) for converting your `KerasCV` model to `diffusers`one. 
+
+`diffusers`repositories on the Hub get a free Inference API and small widgets in the model page where users can play with the model.
+
+```python
+from diffusers import StableDiffusionPipeline
+
+# checkpoint of the converted Stable Diffusion from KerasCV
+model_ckpt = "sayakpaul/text-unet-dogs-kerascv_sd_diffusers_pipeline"
+pipeline = StableDiffusionPipeline.from_pretrained(model_ckpt)
+pipeline.to("cuda")
+
+unique_id = "sks"
+class_label = "dog"
+prompt = f"A photo of {unique_id} {class_label} in a bucket"
+image = pipeline(prompt, num_inference_steps=50).images[0]
+```
+
+## Model Hosting
+
+At the end of [this notebook](https://colab.research.google.com/github/huggingface/community-events/blob/main/keras-dreambooth-sprint/Dreambooth_on_Hub.ipynb) you will see a section dedicated for hosting, and a separate one for inference. We will be using the `huggingface_hub` library’s Keras-specific model pushing and loading functions: `push_to_hub_keras` and `from_pretrained_keras` . We will first push the model using `push_to_hub_keras`. After model is pushed, you will see the model is hosted with a model card like below 👇 
+
+![Repository](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/dreamboothrepo.png)
+
+To version the models better, enable discoverability and reproducibility, we will fill the model card.  Click `Edit model card`. We will first fill the Metadata section of the model card. If your model is trained with a dataset from the Hugging Face Hub, you can fill the datasets section with the dataset. We will provide fill `pipeline_tag` with `text-to-image` and pick a license for our model. 
+
+![Metadata](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/dreambooth-etadata.png)
+
+Then, we will fill the markdown part. Hyperparameters and plot is automatically generated so we can write a short explanation for description, intended use and dataset.
+
+
+You can find the example repository below 👇 
+
+[keras-dreambooth/dreambooth_diffusion_model](https://huggingface.co/keras-dreambooth/dreambooth_diffusion_model)
+
+## Model Demo
+
+We will use Gradio to build our demos for the models we have trained. With `Interface` class it’s straightforward 👇  
+
+```python
+from huggingface_hub import from_pretrained_keras
+from keras_cv import models
+import gradio as gr
+
+sd_dreambooth_model = models.StableDiffusion(
+    img_width=512, img_height=512
+)
+db_diffusion_model = from_pretrained_keras("merve/dreambooth_diffusion_model")
+sd_dreambooth_model._diffusion_model = db_diffusion_model
+
+# generate images
+def infer(prompt):
+    generated_images = sd_dreambooth_model.text_to_image(
+        prompt
+    )
+    return generated_images 
+    
+    
+output = gr.Gallery(label="Outputs").style(grid=(2,2))
+
+# pass function, input type for prompt, the output for multiple images
+gr.Interface(infer, inputs=["text"], outputs=[output]).launch()
+```
+
+You can check out `app.py`file of the application below and repurpose it for your model!
+
+[Dreambooth Submission - a Hugging Face Space by keras-dreambooth](https://huggingface.co/spaces/keras-dreambooth/example-submission)
+
+This app generates images of a corgi 🐶 
+
+![Dreambooth App](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/dreambooth_corgi.png)
+
+## Hosting the Demo on Spaces
+
+After our application is written, we can create a Hugging Face Space to host our app. You can go to [huggingface.co](http://huggingface.co), click on your profile on top right and select “New Space”.
+
+![New Space](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/new_space.png)
+
+
+We can name our Space, pick a license and select Space SDK as “Gradio”. 
+
+![Space Configuration](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/space_config.png)
+
+After creating the Space, you can use either the instructions below to clone the repository locally, adding your files and push, OR, graphical interface to create the files and write the code in the browser.
+
+![Spaces Landing](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/repository_landing.png)
+
+To upload your application file, pick “Add File” and drag and drop your file.
+
+![New Space Landing](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/add_file.png)
+
+Lastly, we have to create a file called `requirements.txt` and add requirements of Dreambooth project like below: 
+
+```
+keras-cv
+tensorflow
+huggingface-hub
+```
+
+And your app should be up and running!
+
+We will host our models and Spaces under [this organization](https://huggingface.co/keras-dreambooth). You can carry your models and Spaces on the settings tab under `Rename or transfer this model` and select `keras-dreambooth` from the dropdown. 
+
+If you don't see `keras-dreambooth` in the dropdown, it's likely that you aren't a member of the organization. Use [this link](https://huggingface.co/organizations/keras-dreambooth/share/bfDDnByLbvPRYypHNUoZJgBgbgtTEYYgVl) to request to join the organization.
+
+## Submission 
+
+You can make submission in three themes: 
+
+- Nature and Animals (`nature`)
+- Sci-fi/Fantasy Universes (`sci-fi`)
+- Consentful (`consentful`): Partner up with an artist to fine-tune on their style, with their consent! Make sure to include a reference to the artist’s express consent (e.g. a tweet) in your model card.
+- Wild Card (`wild-card`): If your submission belongs to any category that is not above, feel free to tag it with wild-card so we can evaluate it out of that category.
+
+
+Add the category with their IDs to the model cards for submission and add `keras-dreambooth` to model card metadata in tags section. Here's an example [model card](https://huggingface.co/spaces/keras-dreambooth/example-submission/blob/main/README.md). All the submissions will be populated [in this leaderboard](https://huggingface.co/spaces/keras-dreambooth/leaderboard) and ranked according to likes on a given Space to determine the winners.
+
+## Sprint **Prizes**
+
+We will pick three winners among the applications submitted, according to the number of likes given to a Space in a given category. 
+
+🛍️ First place will win a 100$ voucher on [hf.co/shop](http://hf.co/shop) or one year subscription to [Hugging Face Pro](https://huggingface.co/pricing#pro)
+
+🛍️ Second place will win a 50$ voucher on [hf.co/shop](http://hf.co/shop) or [the book](https://transformersbook.com/) “Natural Language Processing with Transformers”
+
+🛍️ Third place will win a 30$ voucher on [hf.co/shop](http://hf.co/shop) or three months subscription to [Hugging Face Pro](https://huggingface.co/pricing#pro)
diff --git a/keras-dreambooth-sprint/compute-with-lambda.md b/keras-dreambooth-sprint/compute-with-lambda.md
new file mode 100644
index 0000000000000000000000000000000000000000..b8944a148336e9af9e014d25459f1a69780c4394
--- /dev/null
+++ b/keras-dreambooth-sprint/compute-with-lambda.md
@@ -0,0 +1,196 @@
+## Launch a Lambda Cloud GPU
+Where possible, we encourage you to fine-tune Dreambooth on a local GPU machine. This will mean a faster set-up and more familiarity with your device. 
+
+The training scripts can also be run as a notebook through Google Colab. We recommend you train on Google Colab if you have a "Colab Pro" or "Pro+" subscription. This is to ensure that you receive a sufficiently powerful GPU on your Colab for fine-tuning Stable Diffusion. 
+
+If you do not have access to a local GPU or Colab Pro/Pro+, we'll endeavour to provide you with a cloud GPU instance.
+We've partnered up with Lambda to provide cloud compute for this event. They'll be providing the NVIDIA A10 24 GB GPUs. The Lambda API makes it easy to spin-up and launch a GPU instance. In this section, we'll go through the steps for spinning up an instance one-by-one.
+
+<p align="center" width="100%">
+    <img width="50%" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hf_lambda.png">
+</p>
+
+This section is split into three parts:
+
+- [Launch a Lambda Cloud GPU](#launch-a-lambda-cloud-gpu)
+  - [Signing-Up with Lambda](#signing-up-with-lambda)
+  - [Creating a Cloud Instance](#creating-a-cloud-instance)
+- [Setting up your environment](#setting-up-your-environment)
+  - [Deleting a Cloud Instance](#deleting-a-cloud-instance)
+
+### Signing-Up with Lambda
+
+1. Create an account with Lambda using your email address of choice: http://lambdalabs.com/HF-dreambooth-signup. If you already have an account, skip to step 2.
+2. Using this same email address, email `cloud@lambdal.com` with the Subject line: `Lambda cloud account for HuggingFace Keras DreamBooth - payment authentication and credit request`.
+3. Each user who emails as above will receive $20 in credits (amounting to 60 fine-tuning runs/30 hours of A10).
+4. Register a valid payment method with Lambda in order to redeem the credits (see instructions below).
+
+To redeem these credits, you will need to authorise a valid payment method with Lambda. Provided that you remain within $20 of compute spending, your card **will not** be charged 💸. Registering your card with Lambda is a mandatory sign-up step that we unfortunately cannot bypass. But we reiterate: you will not be charged provided you remain within $20 of compute.
+
+Follow steps 1-4 in the next section [Creating a Cloud Instance](#creating-a-cloud-instance) to register your card. If you experience issues with registering your card, contact the Lambda team on Discord (see [Communications and Problems](#communication-and-problems)).
+
+In order to maximise the free GPU hours you have available for training, we advise that you shut down GPUs when you are not using them and closely monitor your GPU usage. We've detailed the steps you can follow to achieve this in [Deleting a Cloud Instance](#deleting-a-cloud-instance).
+
+### Creating a Cloud Instance
+Estimated time to complete: 5 mins
+
+*You can also follow our video tutorial to set up a cloud instance on Lambda* 👉️ [YouTube Video](https://www.youtube.com/watch?v=Ndm9CROuk5g&list=PLo2EIpI_JMQtncHQHdHq2cinRVk_VZdGW)
+
+1. Click the link: http://lambdalabs.com/HF-dreambooth-instances
+2. You'll be asked to sign in to your Lambda account (if you haven't done so already).
+3. Once on the GPU instance page, click the purple button "Launch instance" in the top right.
+4. Verify a payment method if you haven't done so already. IMPORTANT: if you have followed the instructions in the previous section, you will have received $20 in GPU credits. Exceeding 25 hours of 1x A10 usage may incur charges on your credit card. Contact the Lambda team on Discord if you have issues authenticating your payment method (see [Communications and Problems](#communication-and-problems))
+5. Launching an instance:
+   1. In "Instance type", select the instance type "1x A10 (24 GB PCle)". In case you run out or memory while training, come back here and choose instance of type "1x A100(40GB PCIe)" or "1x A100(40GB SXM4)".
+   2. In "Select region", select the region with availability closest to you.
+   3. In "Select filesystem", select "Don't attach a filesystem".
+6. You will be asked to provide your public SSH key. This will allow you to SSH into the GPU device from your local machine.
+   1. If you’ve not already created an SSH key pair, you can do so with the following command from your local device: 
+      ```bash
+      ssh-keygen
+      ```
+   2. You can find your public SSH key using the command: 
+      ```bash
+      cat ~/.ssh/id_rsa.pub
+      ```
+      (Windows: `type C:UsersUSERNAME.sshid_rsa.pub` where `USERNAME` is the name of your user)
+   4. Copy and paste the output of this command into the first text box
+   5. Give your SSH key a memorable name (e.g. `merve-ssh-key`)
+   6. Click "Add SSH Key"
+7. Select the SSH key from the drop-down menu and click "Launch instance"
+8. Read the terms of use and agree
+9. We can now see on the "GPU instances" page that our device is booting up!
+10. Once the device status changes to "✅ Running", click on the SSH login ("ssh ubuntu@..."). This will copy the SSH login to your clipboard.
+11. Now open a new command line window, paste the SSH login, and hit Enter.
+12. If asked "Are you sure you want to continue connecting?", type "yes" and press Enter.
+13. Great! You're now SSH'd into your A10 device! We're now ready to set up our Python environment!
+
+You can see your total GPU usage from the Lambda cloud interface: https://cloud.lambdalabs.com/usage
+
+Here, you can see the total charges that you have incurred since the start of the event. We advise that you check your total on a daily basis to make sure that it remains below the credit allocation of $20. This ensures that you are not inadvertently charged for GPU hours.
+
+If you are unable to SSH into your Lambda GPU in step 11, there is a workaround that you can try. On the [GPU instances page](http://lambdalabs.com/HF-dreambooth-instances), under the column "Cloud IDE", click the button "Launch". This will launch a Jupyter Lab on your GPU which will be displayed in your browser. In the top left-hand corner, click "File" -> "New" -> "Terminal". This will open up a new terminal window. You can use this terminal window to set up Python environment and install dependencies and run scripts.
+
+
+## Setting up your environment
+
+You can establish an SSH tunnel to your instance using below command: 
+```
+ssh ubuntu@ADDRESS_OF_INSTANCE -L 8888:localhost:8888
+```
+This will establish the tunnel to a remote machine and also forward the SSH port to a local port, so you can open a jupyter notebook on the remote machine and access it from your own local machine. 
+We will use **TensorFlow** and **Keras CV** to train DreamBooth model, and later use **diffusers** for conversion. In this section, we'll cover how to set up an environment with the required libraries. This section assumes that you are SSH'd into your GPU device. 
+
+You can setup your environment like below. 
+Below script:
+1. Creates a python virtual environment,
+2. Installs the requirements,
+3. Does authentication for Hugging Face. 
+After you run `huggingface-cli login`, pass your write token that you can get from [here](https://huggingface.co/settings/tokens). This will authenticate you to push your models to Hugging Face Hub.
+
+We will use conda for this (follow this especially if you are training on A10). Install miniconda like below:
+```bash
+sudo wget -c https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
+sudo chmod +x Miniconda3-latest-Linux-x86_64.sh && ./Miniconda3-latest-Linux-x86_64.sh
+```
+Accept the terms by typing "yes", confirm the path by pressing enter and then confirm `conda init` by typing in yes again.
+To make conda commands accessible in the current shell environment enter:
+```bash
+source ~/.bashrc
+```
+Disable the base virtual conda environment:
+```bash
+conda config --set auto_activate_base false
+conda deactivate
+```
+Now activate conda and create your own environment (in this example we use `my_env` for simplicity).
+```bash
+conda create -n my_env python==3.10
+conda activate my_env
+ ```
+As a next step, we may confirm that pip points to the correct path:
+ ```bash
+ which pip
+ ```
+The path should point to `/home/ubuntu/miniconda3/envs/my_env/bin/pip`.
+
+** Note: Please make sure you are opening the notebook either in env (if you are using Python virtual environment by following above commands) or use ipykernel to add your environment to jupyter. For first one, you can get into env folder itself and create your notebook there and it should work.**
+
+As a next step, we need to install necessary dependencies for CUDA Support to work properly and getting a jupyter notebook running. Ensure you are inside the `my_env` conda environment you created previously:
+```bash
+conda install nb_conda_kernels
+ipython kernel install --user --name=my_env
+conda install -c conda-forge cudatoolkit=11.8.0
+python3 -m pip install nvidia-cudnn-cu11==8.6.0.163
+```
+Next you need to setup XLA to the correct CUDA library path with following command:
+ ```bash
+export XLA_FLAGS=--xla_gpu_cuda_data_dir=/usr/lib/cuda
+CUDNN_PATH=$(dirname $(python -c "import nvidia.cudnn;print(nvidia.cudnn.__file__)"))
+export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/:$CUDNN_PATH/lib
+ ```
+ ** Note: you need to set this every time you close and open the terminal via SSH tunnel. If you do not do this, the `fit` method will fail. Please read through the error logs to see where to find the missing library and set the above path accordingly.
+
+Now, we also must install Tensorflow inside our virtual environment. It is recommend, doing so with pip:
+
+ ```bash
+python -m pip install tensorflow==2.12.*
+ ```
+ To confirm the installed version, and the success of setting up our drivers in the conda environment:
+ ```bash
+ python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU')); print(tf.__version__)"
+ ```
+It should return True, and display an array with one physical device. The version should be equal to atleast 2.12.
+
+We may now install the dependendencies necessary for the jupyter notebook:
+ ```bash
+pip install keras_cv===0.4.2 tensorflow_datasets===4.8.1 pillow===9.4.0 imutils opencv-python matplotlib huggingface-hub pycocotools
+ ```
+ 
+Now we can start our jupyter notebook instance:
+```bash
+jupyter notebook
+```
+Enter the URL in the browser or connect through VSCode. If this does not work, you likely forgot to forward the 8888 port.
+When you open jupyter, select your environment `my_env` in `New` dropdown and it will create your notebook with conda environment you've created.
+
+Now inside the notebook:
+
+First check the pip and python are poinitng to right places by running following commands. First check for pip path by running:
+```python
+!which pip
+```
+It should point to `/home/ubuntu/miniconda3/envs/my_env/bin/pip`. If it is pointing to `/home/ubuntu/.local/bin/pip`, you have not have run `conda config --set auto_activate_base false`. Please run it again and activate `my_env` again. Also check that your notebook is running in the proper kernel `my_env`. Once inside the notebook, you can change it from the menu navigation `Kernel->Change Kernel -> my_env`. You should now see `my_env` in the top right of the notebook. 
+
+Now check for python path aswell:
+```python
+!which python
+```
+It should point to: `/home/ubuntu/miniconda3/envs/my_env/bin/python`
+
+Running below line in the notebook makes sure that we have installed the version of TensorFlow that supports GPU, and that TensorFlow can detect the GPUs. If everything goes right, it should return `True` and a list that consists of a GPU. The version should be equal to or greater than 2.11 to support the correct version of keras_cv. In our example, it should print 2.12.
+```python
+import tensorflow as tf
+print(tf.test.is_built_with_cuda())
+print(tf.config.list_logical_devices('GPU'))
+print(tf.__version__)
+```
+
+You can either create your own notebook or clone the notebook `https://github.com/huggingface/community-events/blob/main/keras-dreambooth-sprint/Dreambooth_on_Hub.ipynb` if you haven't done so previously.
+
+You're all set! You can simply launch a jupyter notebook and start training models! 🚀 
+
+### Deleting a Cloud Instance
+
+30 1x A10 hours should provide you with enough time for 60 fine-tuning runs for Dreambooth. To maximise the GPU time you have for training, we advise that you shut down GPUs over prolonged periods of time when they are not in use. So be smart and shut down your GPU when you're not training.
+
+Creating an instance and setting it up for the first time may take up to 20 minutes. Subsequently, this process will be much faster as you gain familiarity with the steps, so you shouldn't worry about having to delete a GPU and spinning one up the next time you need one. You can expect to spin-up and delete 2-3 GPUs over the course of the fine-tuning event.
+
+We'll quickly run through the steps for deleting a Lambda GPU. You can come back to these steps after you've performed your first training run and you want to shut down the GPU:
+
+1. Go to the instances page: http://lambdalabs.com/HF-dreambooth-instances
+2. Click the checkbox on the left next to the GPU device you want to delete
+3. Click the button "Terminate" in the top right-hand side of your screen (under the purple button "Launch instance")
+4. Type "erase data on instance" in the text box and press "ok"
+
+Your GPU device is now deleted and will stop consuming GPU credits.
diff --git a/keras-dreambooth-sprint/requirements.txt b/keras-dreambooth-sprint/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f6ec66bab9f8aa64ccad2dbc9394e7cc70f10e8d
--- /dev/null
+++ b/keras-dreambooth-sprint/requirements.txt
@@ -0,0 +1,7 @@
+keras_cv==0.4.0
+tensorflow>=2.10.0
+tensorflow_datasets>=4.8.1
+pillow==9.4.0
+imutils
+opencv-python
+huggingface-hub[cli]
\ No newline at end of file
diff --git a/keras-sprint/README.md b/keras-sprint/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..9d5ea17583ce399a60ba0cea487b7e671b432ffa
--- /dev/null
+++ b/keras-sprint/README.md
@@ -0,0 +1,12 @@
+# Official Repository for Keras Sprint Event
+
+![](https://huggingface.co/spaces/keras-io/README/resolve/main/keras-hf.png)
+
+Keras Sprint by Hugging Face aims to reproduce [official Keras examples](https://keras.io/examples/) and build demos to them on [Hugging Face Spaces](https://huggingface.co/spaces).
+
+Here you can find examples to guide you for the sprint. It contains two end-to-end examples of a successful submission for the event. 
+
+## Useful Resources
+- To learn more about Keras sprint, check out [contribution guide](https://huggingface2.notion.site/Keras-Sprint-Contribution-Guide-ab1543412f3a4f7194896d6048585676).
+- To join the sprint, join our [discord](https://huggingface.co/join/discord), head to #keras-working-group channel and take one of the available examples from [this spreadsheet](https://docs.google.com/spreadsheets/d/1EG6z4mmeBzmMidUzDdSDr02quBs2BcgjNOrtZCwnqvs/edit#gid=1687823618) by commenting on it.
+- Check out our previous work at [Keras Hugging Face organization](https://huggingface.co/keras-io) and [official Keras examples](https://keras.io/examples/).
\ No newline at end of file
diff --git a/keras-sprint/deeplabv3_plus.ipynb b/keras-sprint/deeplabv3_plus.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..c3706ae89f089008c634ac0a3b88b8078ec9d102
--- /dev/null
+++ b/keras-sprint/deeplabv3_plus.ipynb
@@ -0,0 +1,3919 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/merveenoyan/community-events/blob/main/deeplabv3_plus.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0Nm2YOjeNK6L"
+      },
+      "source": [
+        "# Multiclass semantic segmentation using DeepLabV3+\n",
+        "\n",
+        "This is an example notebook for Keras sprint prepared by Hugging Face. Keras Sprint aims to reproduce Keras examples and build interactive demos to them.\n",
+        "The markdown parts beginning with 🤗 and the following code snippets are the parts added by Hugging Face team to give you an example of how to host your model and build a demo.\n",
+        "\n",
+        "\n",
+        "**Original Author of the DeepLabV3 Example:** [Soumik Rakshit](http://github.com/soumik12345)<br>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "D8YsmvyyNK6P"
+      },
+      "source": [
+        "## Introduction\n",
+        "\n",
+        "Semantic segmentation, with the goal to assign semantic labels to every pixel in an image,\n",
+        "is an essential computer vision task. In this example, we implement\n",
+        "the **DeepLabV3+** model for multi-class semantic segmentation, a fully-convolutional\n",
+        "architecture that performs well on semantic segmentation benchmarks.\n",
+        "\n",
+        "### References:\n",
+        "\n",
+        "- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://arxiv.org/pdf/1802.02611.pdf)\n",
+        "- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://arxiv.org/abs/1706.05587)\n",
+        "- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://arxiv.org/abs/1606.00915)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "y0vaTgH-NK6Q"
+      },
+      "source": [
+        "## Downloading the data\n",
+        "\n",
+        "We will use the [Crowd Instance-level Human Parsing Dataset](https://arxiv.org/abs/1811.12596)\n",
+        "for training our model. The Crowd Instance-level Human Parsing (CIHP) dataset has 38,280 diverse human images.\n",
+        "Each image in CIHP is labeled with pixel-wise annotations for 20 categories, as well as instance-level identification.\n",
+        "This dataset can be used for the \"human part segmentation\" task."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "dRBW6fvgNK6Q"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import cv2\n",
+        "import numpy as np\n",
+        "from glob import glob\n",
+        "from scipy.io import loadmat\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "import tensorflow as tf\n",
+        "from tensorflow import keras\n",
+        "from tensorflow.keras import layers"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lV4WmyoXTLaK",
+        "outputId": "afb73da5-807c-4620-d557-d2fa5d1e8374"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Find dataset here: https://drive.google.com/uc?id=1B9A9UCJYMwTL4oBEo4RZfbMZMaZhKJaz"
+      ],
+      "metadata": {
+        "id": "CDeJjizwThUD"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "itOy9ZhnNK6R"
+      },
+      "outputs": [],
+      "source": [
+        "!unzip -q /content/drive/MyDrive/instance-level-human-parsing.zip"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 🤗 Installing packages to host and build a demo to our models"
+      ],
+      "metadata": {
+        "id": "q_A0QgojPTGj"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install huggingface-hub\n",
+        "!pip install gradio"
+      ],
+      "metadata": {
+        "id": "fBFHUsE2PYEw"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZgFk68VYNK6S"
+      },
+      "source": [
+        "## Creating a TensorFlow Dataset\n",
+        "\n",
+        "Training on the entire CIHP dataset with 38,280 images takes a lot of time, hence we will be using\n",
+        "a smaller subset of 200 images for training our model in this example."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ov8dbQBqNK6S",
+        "outputId": "7e540f83-c1bf-44e0-f68a-970e1e3dcb45",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Train Dataset: <BatchDataset element_spec=(TensorSpec(shape=(4, 512, 512, 3), dtype=tf.float32, name=None), TensorSpec(shape=(4, 512, 512, 1), dtype=tf.float32, name=None))>\n",
+            "Val Dataset: <BatchDataset element_spec=(TensorSpec(shape=(4, 512, 512, 3), dtype=tf.float32, name=None), TensorSpec(shape=(4, 512, 512, 1), dtype=tf.float32, name=None))>\n"
+          ]
+        }
+      ],
+      "source": [
+        "IMAGE_SIZE = 512\n",
+        "BATCH_SIZE = 4\n",
+        "NUM_CLASSES = 20\n",
+        "DATA_DIR = \"./instance-level_human_parsing/instance-level_human_parsing/Training\"\n",
+        "NUM_TRAIN_IMAGES = 1000\n",
+        "NUM_VAL_IMAGES = 50\n",
+        "\n",
+        "train_images = sorted(glob(os.path.join(DATA_DIR, \"Images/*\")))[:NUM_TRAIN_IMAGES]\n",
+        "train_masks = sorted(glob(os.path.join(DATA_DIR, \"Category_ids/*\")))[:NUM_TRAIN_IMAGES]\n",
+        "val_images = sorted(glob(os.path.join(DATA_DIR, \"Images/*\")))[\n",
+        "    NUM_TRAIN_IMAGES : NUM_VAL_IMAGES + NUM_TRAIN_IMAGES\n",
+        "]\n",
+        "val_masks = sorted(glob(os.path.join(DATA_DIR, \"Category_ids/*\")))[\n",
+        "    NUM_TRAIN_IMAGES : NUM_VAL_IMAGES + NUM_TRAIN_IMAGES\n",
+        "]\n",
+        "\n",
+        "\n",
+        "def read_image(image_path, mask=False):\n",
+        "    image = tf.io.read_file(image_path)\n",
+        "    if mask:\n",
+        "        image = tf.image.decode_png(image, channels=1)\n",
+        "        image.set_shape([None, None, 1])\n",
+        "        image = tf.image.resize(images=image, size=[IMAGE_SIZE, IMAGE_SIZE])\n",
+        "    else:\n",
+        "        image = tf.image.decode_png(image, channels=3)\n",
+        "        image.set_shape([None, None, 3])\n",
+        "        image = tf.image.resize(images=image, size=[IMAGE_SIZE, IMAGE_SIZE])\n",
+        "        image = image / 127.5 - 1\n",
+        "    return image\n",
+        "\n",
+        "\n",
+        "def load_data(image_list, mask_list):\n",
+        "    image = read_image(image_list)\n",
+        "    mask = read_image(mask_list, mask=True)\n",
+        "    return image, mask\n",
+        "\n",
+        "\n",
+        "def data_generator(image_list, mask_list):\n",
+        "    dataset = tf.data.Dataset.from_tensor_slices((image_list, mask_list))\n",
+        "    dataset = dataset.map(load_data, num_parallel_calls=tf.data.AUTOTUNE)\n",
+        "    dataset = dataset.batch(BATCH_SIZE, drop_remainder=True)\n",
+        "    return dataset\n",
+        "\n",
+        "\n",
+        "train_dataset = data_generator(train_images, train_masks)\n",
+        "val_dataset = data_generator(val_images, val_masks)\n",
+        "\n",
+        "print(\"Train Dataset:\", train_dataset)\n",
+        "print(\"Val Dataset:\", val_dataset)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "68WUsYO-NK6T"
+      },
+      "source": [
+        "## Building the DeepLabV3+ model\n",
+        "\n",
+        "DeepLabv3+ extends DeepLabv3 by adding an encoder-decoder structure. The encoder module\n",
+        "processes multiscale contextual information by applying dilated convolution at multiple\n",
+        "scales, while the decoder module refines the segmentation results along object boundaries.\n",
+        "\n",
+        "![](https://github.com/lattice-ai/DeepLabV3-Plus/raw/master/assets/deeplabv3_plus_diagram.png)\n",
+        "\n",
+        "**Dilated convolution:** With dilated convolution, as we go deeper in the network, we can keep the\n",
+        "stride constant but with larger field-of-view without increasing the number of parameters\n",
+        "or the amount of computation. Besides, it enables larger output feature maps, which is\n",
+        "useful for semantic segmentation.\n",
+        "\n",
+        "The reason for using **Dilated Spatial Pyramid Pooling** is that it was shown that as the\n",
+        "sampling rate becomes larger, the number of valid filter weights (i.e., weights that\n",
+        "are applied to the valid feature region, instead of padded zeros) becomes smaller."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "kh9auWU-NK6U"
+      },
+      "outputs": [],
+      "source": [
+        "\n",
+        "def convolution_block(\n",
+        "    block_input,\n",
+        "    num_filters=256,\n",
+        "    kernel_size=3,\n",
+        "    dilation_rate=1,\n",
+        "    padding=\"same\",\n",
+        "    use_bias=False,\n",
+        "):\n",
+        "    x = layers.Conv2D(\n",
+        "        num_filters,\n",
+        "        kernel_size=kernel_size,\n",
+        "        dilation_rate=dilation_rate,\n",
+        "        padding=\"same\",\n",
+        "        use_bias=use_bias,\n",
+        "        kernel_initializer=keras.initializers.HeNormal(),\n",
+        "    )(block_input)\n",
+        "    x = layers.BatchNormalization()(x)\n",
+        "    return tf.nn.relu(x)\n",
+        "\n",
+        "\n",
+        "def DilatedSpatialPyramidPooling(dspp_input):\n",
+        "    dims = dspp_input.shape\n",
+        "    x = layers.AveragePooling2D(pool_size=(dims[-3], dims[-2]))(dspp_input)\n",
+        "    x = convolution_block(x, kernel_size=1, use_bias=True)\n",
+        "    out_pool = layers.UpSampling2D(\n",
+        "        size=(dims[-3] // x.shape[1], dims[-2] // x.shape[2]), interpolation=\"bilinear\",\n",
+        "    )(x)\n",
+        "\n",
+        "    out_1 = convolution_block(dspp_input, kernel_size=1, dilation_rate=1)\n",
+        "    out_6 = convolution_block(dspp_input, kernel_size=3, dilation_rate=6)\n",
+        "    out_12 = convolution_block(dspp_input, kernel_size=3, dilation_rate=12)\n",
+        "    out_18 = convolution_block(dspp_input, kernel_size=3, dilation_rate=18)\n",
+        "\n",
+        "    x = layers.Concatenate(axis=-1)([out_pool, out_1, out_6, out_12, out_18])\n",
+        "    output = convolution_block(x, kernel_size=1)\n",
+        "    return output\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c0I36dMgNK6V"
+      },
+      "source": [
+        "The encoder features are first bilinearly upsampled by a factor 4, and then\n",
+        "concatenated with the corresponding low-level features from the network backbone that\n",
+        "have the same spatial resolution. For this example, we\n",
+        "use a ResNet50 pretrained on ImageNet as the backbone model, and we use\n",
+        "the low-level features from the `conv4_block6_2_relu` block of the backbone."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "1RhOhvaQNK6V"
+      },
+      "outputs": [],
+      "source": [
+        "\n",
+        "def DeeplabV3Plus(image_size, num_classes):\n",
+        "    model_input = keras.Input(shape=(image_size, image_size, 3))\n",
+        "    resnet50 = keras.applications.ResNet50(\n",
+        "        weights=\"imagenet\", include_top=False, input_tensor=model_input\n",
+        "    )\n",
+        "    x = resnet50.get_layer(\"conv4_block6_2_relu\").output\n",
+        "    x = DilatedSpatialPyramidPooling(x)\n",
+        "\n",
+        "    input_a = layers.UpSampling2D(\n",
+        "        size=(image_size // 4 // x.shape[1], image_size // 4 // x.shape[2]),\n",
+        "        interpolation=\"bilinear\",\n",
+        "    )(x)\n",
+        "    input_b = resnet50.get_layer(\"conv2_block3_2_relu\").output\n",
+        "    input_b = convolution_block(input_b, num_filters=48, kernel_size=1)\n",
+        "\n",
+        "    x = layers.Concatenate(axis=-1)([input_a, input_b])\n",
+        "    x = convolution_block(x)\n",
+        "    x = convolution_block(x)\n",
+        "    x = layers.UpSampling2D(\n",
+        "        size=(image_size // x.shape[1], image_size // x.shape[2]),\n",
+        "        interpolation=\"bilinear\",\n",
+        "    )(x)\n",
+        "    model_output = layers.Conv2D(num_classes, kernel_size=(1, 1), padding=\"same\")(x)\n",
+        "    return keras.Model(inputs=model_input, outputs=model_output)\n",
+        "\n",
+        "\n",
+        "model = DeeplabV3Plus(image_size=IMAGE_SIZE, num_classes=NUM_CLASSES)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 🤗 Before we move on with training, let's create TensorBoard"
+      ],
+      "metadata": {
+        "id": "uFkYSXWSSRRh"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Load the TensorBoard notebook extension\n",
+        "%load_ext tensorboard"
+      ],
+      "metadata": {
+        "id": "ICOJLuIwSQjz"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "log_dir = \"logs/fit/\"\n",
+        "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)"
+      ],
+      "metadata": {
+        "id": "ViDhtqnvSX_q"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oTLle3r1NK6W"
+      },
+      "source": [
+        "## Training\n",
+        "\n",
+        "We train the model using sparse categorical crossentropy as the loss function, and\n",
+        "Adam as the optimizer."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "dnjSoXIbNK6W",
+        "outputId": "1af1a02f-5ec4-473a-f6bd-32c7506f158e",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/5\n",
+            "250/250 [==============================] - 40s 85ms/step - loss: 1.2065 - accuracy: 0.6356 - val_loss: 2.5502 - val_accuracy: 0.5545\n",
+            "Epoch 2/5\n",
+            "250/250 [==============================] - 20s 81ms/step - loss: 0.9568 - accuracy: 0.6963 - val_loss: 2.6706 - val_accuracy: 0.5978\n",
+            "Epoch 3/5\n",
+            "250/250 [==============================] - 20s 81ms/step - loss: 0.8468 - accuracy: 0.7285 - val_loss: 1.4310 - val_accuracy: 0.6123\n",
+            "Epoch 4/5\n",
+            "250/250 [==============================] - 20s 81ms/step - loss: 0.7736 - accuracy: 0.7513 - val_loss: 1.0082 - val_accuracy: 0.6894\n",
+            "Epoch 5/5\n",
+            "250/250 [==============================] - 20s 81ms/step - loss: 0.7116 - accuracy: 0.7708 - val_loss: 1.0158 - val_accuracy: 0.7053\n"
+          ]
+        }
+      ],
+      "source": [
+        "loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
+        "model.compile(\n",
+        "    optimizer=keras.optimizers.Adam(learning_rate=0.001),\n",
+        "    loss=loss,\n",
+        "    metrics=[\"accuracy\"],\n",
+        ")\n",
+        "\n",
+        "history = model.fit(train_dataset, validation_data=val_dataset, epochs=5,\n",
+        "                        callbacks = [tensorboard_callback])\n",
+        "# 🤗 note how we call tensorboard"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 🤗 Push your model to the Hub\n",
+        "We will push our model to the Hugging Face Hub with tensorboard logs.\n",
+        "\n",
+        "If you already have access to keras-io organization, you can give \"keras-io/deeplab-v3\" as the model ID. If not, you can push model to your own account and then carry it to the keras-io organization later. 🥳\n",
+        "\n",
+        "To push your models to the Hub, you need authentication. To authenticate, you can log using notebook_login. You can get your token from https://huggingface.co/settings/tokens 🙌🏻"
+      ],
+      "metadata": {
+        "id": "QLwNPNyZVmjM"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from huggingface_hub import notebook_login\n",
+        "notebook_login()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 387,
+          "referenced_widgets": [
+            "6656427d58584fdc93d205920dd586c0",
+            "bbcfbb2e927d453ca5ab6f58c0096ce8",
+            "5c7a3a49f4d64fe7917c8c489047888a",
+            "c8737dd8a7a44ee08762f4a66f8e62c2",
+            "2b5694f714f5449d880fea1e59bf2496",
+            "65c7d524f74444d8becc6b2249c61dfa",
+            "ea738912885747fb85c1c45902128658",
+            "86853c4a023c4eae98828688480d4ee1",
+            "ef117ade52df431eb27301e058d7e649",
+            "b0d5f1a62eaf42d89639b2d3b77000c2",
+            "351c270c0e9a47e1a2c5755f470ad065",
+            "9a20e3b8c7ce4290b60ce1889a369a8a",
+            "83bb5d69285f43fbb30d2a09f8459f06",
+            "338a72e0d5834c2f9879591e295d2b7c",
+            "e99ed10952c5407b90ac9371f41f1e79",
+            "160e8e1d73a041f1bfab72f0bbc7bcbc",
+            "f49bb981eda24cdb897d95e6a9541cf9"
+          ]
+        },
+        "id": "sONm33JwcGFc",
+        "outputId": "858ce119-a678-4d25-9393-6dde6824153d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Login successful\n",
+            "Your token has been saved to /root/.huggingface/token\n",
+            "\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n",
+            "You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n",
+            "\n",
+            "git config --global credential.helper store\u001b[0m\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now we can push our model to the Hugging Face Hub 🤩🙌🏻 The below function will:\n",
+        "1. create a remote repository on Hugging Face Hub, \n",
+        "2. serialize our model, \n",
+        "3. create a model card including training hyperparameters, model architecture and couple of fields you can fill about model,\n",
+        "4. push the model to the Hub."
+      ],
+      "metadata": {
+        "id": "V23tNN0JcLIa"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from huggingface_hub import push_to_hub_keras\n",
+        "\n",
+        "push_to_hub_keras(model,\n",
+        "    \"merve/deeplab-v3\",\n",
+        "    log_dir = \"./logs/fit\",\n",
+        "    tags = [\"image-segmentation\"]\n",
+        ")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 508,
+          "referenced_widgets": [
+            "79d25547eafe436cb17eacea753d8d6b",
+            "590f529278d94fc3b5a8d7bafca4522b",
+            "d935575e705f4be3a7bd741ec52ad3ee",
+            "c92ae6434a7a48b9847047075724dc07",
+            "4a5c0fbb5b7f42b2b72c4ad6dae84bba",
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+            "012c339e2a8a4792b58b2283f81a10a6",
+            "4d741d8aea5d488687b44d2c5251aafa",
+            "3fc5074bdf8e4d819a7e658e025fd726",
+            "3285a709e0534f25930344074ddab274",
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+          ]
+        },
+        "id": "84D5xQ3PT7kj",
+        "outputId": "33db88da-168b-44c8-d229-77ad96ba443c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Cloning https://huggingface.co/merve/deeplab-v3 into local empty directory.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "INFO:tensorflow:Assets written to: merve/deeplab-v3/assets\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Adding files tracked by Git LFS: ['variables/variables.data-00000-of-00001', 'model.png', 'variables/variables.index']. This may take a bit of time if the files are large.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Upload file variables/variables.data-00000-of-00001:   0%|          | 3.34k/45.4M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
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+              "Upload file logs/validation/events.out.tfevents.1653922035.b3a961ec2aba.109.1.v2: 100%|##########| 1.60k/1.60k…"
+            ],
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "remote: Enforcing permissions...        \n",
+            "remote: Allowed refs: all        \n",
+            "To https://huggingface.co/merve/deeplab-v3\n",
+            "   de58c0e..4c83b32  main -> main\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'https://huggingface.co/merve/deeplab-v3/commit/4c83b323a69683a7e3607fd07d7840309bf4b096'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 25
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 🤗 Build a UI using Gradio"
+      ],
+      "metadata": {
+        "id": "OQ-Yrw40UGa3"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We will now build a UI with Gradio. To do this, we just need to write the inference function (which is usually given in notebooks) and pass it to a gradio `Interface`."
+      ],
+      "metadata": {
+        "id": "3okP5jFMVc9M"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import tensorflow as tf\n",
+        "import gradio as gr\n",
+        "from huggingface_hub import from_pretrained_keras\n",
+        "import cv2\n",
+        "\n",
+        "# note how I download the already pushed model\n",
+        "model = from_pretrained_keras(\"merve/deeplab-v3\")\n",
+        "\n",
+        "\n",
+        "# we will now create our inference function\n",
+        "colormap = np.array([[0,0,0], [31,119,180], [44,160,44], [44, 127, 125], [52, 225, 143],\n",
+        "                    [217, 222, 163], [254, 128, 37], [130, 162, 128], [121, 7, 166], [136, 183, 248],\n",
+        "                    [85, 1, 76], [22, 23, 62], [159, 50, 15], [101, 93, 152], [252, 229, 92],\n",
+        "                    [167, 173, 17], [218, 252, 252], [238, 126, 197], [116, 157, 140], [214, 220, 252]], dtype=np.uint8)\n",
+        "                    \n",
+        "img_size = 512\n",
+        "                    \n",
+        "def read_image(image):\n",
+        "    image = tf.convert_to_tensor(image)\n",
+        "    image.set_shape([None, None, 3])\n",
+        "    image = tf.image.resize(images=image, size=[img_size, img_size])\n",
+        "    image = image / 127.5 - 1\n",
+        "    return image\n",
+        "\n",
+        "def infer(model, image_tensor):\n",
+        "    predictions = model.predict(np.expand_dims((image_tensor), axis=0))\n",
+        "    predictions = np.squeeze(predictions)\n",
+        "    predictions = np.argmax(predictions, axis=2)\n",
+        "    return predictions\n",
+        "\n",
+        "def decode_segmentation_masks(mask, colormap, n_classes):\n",
+        "    r = np.zeros_like(mask).astype(np.uint8)\n",
+        "    g = np.zeros_like(mask).astype(np.uint8)\n",
+        "    b = np.zeros_like(mask).astype(np.uint8)\n",
+        "    for l in range(0, n_classes):\n",
+        "        idx = mask == l\n",
+        "        r[idx] = colormap[l, 0]\n",
+        "        g[idx] = colormap[l, 1]\n",
+        "        b[idx] = colormap[l, 2]\n",
+        "    rgb = np.stack([r, g, b], axis=2)\n",
+        "    return rgb\n",
+        "\n",
+        "def get_overlay(image, colored_mask):\n",
+        "    image = tf.keras.preprocessing.image.array_to_img(image)\n",
+        "    image = np.array(image).astype(np.uint8)\n",
+        "    overlay = cv2.addWeighted(image, 0.35, colored_mask, 0.65, 0)\n",
+        "    return overlay\n",
+        "\n",
+        "def segmentation(input_image):\n",
+        "    image_tensor = read_image(input_image)\n",
+        "    prediction_mask = infer(image_tensor=image_tensor, model=model)\n",
+        "    prediction_colormap = decode_segmentation_masks(prediction_mask, colormap, 20)\n",
+        "    overlay = get_overlay(image_tensor, prediction_colormap)\n",
+        "    return (overlay, prediction_colormap)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "XsjCxx3uUJIa",
+        "outputId": "a3b06994-0e67-4af9-e075-c36713ee4507"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "config.json not found in HuggingFace Hub\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# the app takes one image to be segmented\n",
+        "input = gr.inputs.Image()\n",
+        "# the app outputs two segmented images\n",
+        "output = [gr.outputs.Image(), gr.outputs.Image()]\n",
+        "# it's good practice to pass examples, description and a title to guide users\n",
+        "examples = [[\"/content/example_image_2.jpeg\"], [\"/content/example_image_3.jpeg\"]] \n",
+        "title = \"Human Part Segmentation\"\n",
+        "description = \"Upload an image or select from examples to segment out different human parts.\"\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "b8gY7vvDWamC",
+        "outputId": "44409207-5a0f-434a-be4b-e5e8b29a203a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/gradio/deprecation.py:40: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
+            "  warnings.warn(value)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Let's launch the interface!"
+      ],
+      "metadata": {
+        "id": "j_Y53qbnWf2d"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "gr.Interface(segmentation, input, output, examples=examples, allow_flagging=False, analytics_enabled=False,\n",
+        "  title=title, description=description).launch(enable_queue=True)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 704
+        },
+        "id": "R8CpugHGWfDQ",
+        "outputId": "4cb4e0f2-d75b-41f3-a78b-969537f91ee9"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/gradio/interface.py:356: UserWarning: The `allow_flagging` parameter in `Interface` nowtakes a string value ('auto', 'manual', or 'never'), not a boolean. Setting parameter to: 'never'.\n",
+            "  \"The `allow_flagging` parameter in `Interface` now\"\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Colab notebook detected. To show errors in colab notebook, set `debug=True` in `launch()`\n",
+            "Running on public URL: https://38608.gradio.app\n",
+            "\n",
+            "This share link expires in 72 hours. For free permanent hosting, check out Spaces (https://huggingface.co/spaces)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
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+              "\n",
+              "        <iframe\n",
+              "            width=\"900\"\n",
+              "            height=\"500\"\n",
+              "            src=\"https://38608.gradio.app\"\n",
+              "            frameborder=\"0\"\n",
+              "            allowfullscreen\n",
+              "        ></iframe>\n",
+              "        "
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+              " 'http://127.0.0.1:7862/',\n",
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diff --git a/keras-sprint/mnist_convnet.ipynb b/keras-sprint/mnist_convnet.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..9cc01bb5ad68d0a386c49215ecb44b5de075dcf4
--- /dev/null
+++ b/keras-sprint/mnist_convnet.ipynb
@@ -0,0 +1,998 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KwORmaB27LPx"
+      },
+      "source": [
+        "# Simple MNIST convnet\n",
+        "\n",
+        "This example shows how to push your Keras model to the Hugging Face Hub and load the model from Hub.\n",
+        "\n",
+        "**Original Author of Example:** [fchollet](https://twitter.com/fchollet)<br>\n",
+        "**Description:** A simple convnet that achieves ~99% test accuracy on MNIST."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "860CEXn27LP1"
+      },
+      "source": [
+        "## Setup"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "id": "7Msic2JB7LP1"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "from tensorflow import keras\n",
+        "from tensorflow.keras import layers"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "🤗 Install Hugging Face Hub"
+      ],
+      "metadata": {
+        "id": "4s6WujK7ILKt"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install huggingface_hub"
+      ],
+      "metadata": {
+        "id": "JNzv7-Cg_cgu"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import huggingface_hub\n",
+        "from huggingface_hub import notebook_login, push_to_hub_keras, from_pretrained_keras"
+      ],
+      "metadata": {
+        "id": "HS4vW65V_-G-"
+      },
+      "execution_count": 21,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "00LCkZPt7LP3"
+      },
+      "source": [
+        "## Prepare the data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "j6tx7Fkh7LP3",
+        "outputId": "48ae4179-4665-4938-9a10-2652cc464bdf"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "x_train shape: (60000, 28, 28, 1)\n",
+            "60000 train samples\n",
+            "10000 test samples\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Model / data parameters\n",
+        "num_classes = 10\n",
+        "input_shape = (28, 28, 1)\n",
+        "\n",
+        "# the data, split between train and test sets\n",
+        "(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n",
+        "\n",
+        "# Scale images to the [0, 1] range\n",
+        "x_train = x_train.astype(\"float32\") / 255\n",
+        "x_test = x_test.astype(\"float32\") / 255\n",
+        "# Make sure images have shape (28, 28, 1)\n",
+        "x_train = np.expand_dims(x_train, -1)\n",
+        "x_test = np.expand_dims(x_test, -1)\n",
+        "print(\"x_train shape:\", x_train.shape)\n",
+        "print(x_train.shape[0], \"train samples\")\n",
+        "print(x_test.shape[0], \"test samples\")\n",
+        "\n",
+        "\n",
+        "# convert class vectors to binary class matrices\n",
+        "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
+        "y_test = keras.utils.to_categorical(y_test, num_classes)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5bpZgm6n7LP4"
+      },
+      "source": [
+        "## Build the model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "QI34HRui7LP4",
+        "outputId": "a0a91950-828e-45a9-d10c-bc7f05cb742e"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Model: \"sequential\"\n",
+            "_________________________________________________________________\n",
+            " Layer (type)                Output Shape              Param #   \n",
+            "=================================================================\n",
+            " conv2d (Conv2D)             (None, 26, 26, 32)        320       \n",
+            "                                                                 \n",
+            " max_pooling2d (MaxPooling2D  (None, 13, 13, 32)       0         \n",
+            " )                                                               \n",
+            "                                                                 \n",
+            " conv2d_1 (Conv2D)           (None, 11, 11, 64)        18496     \n",
+            "                                                                 \n",
+            " max_pooling2d_1 (MaxPooling  (None, 5, 5, 64)         0         \n",
+            " 2D)                                                             \n",
+            "                                                                 \n",
+            " flatten (Flatten)           (None, 1600)              0         \n",
+            "                                                                 \n",
+            " dropout (Dropout)           (None, 1600)              0         \n",
+            "                                                                 \n",
+            " dense (Dense)               (None, 10)                16010     \n",
+            "                                                                 \n",
+            "=================================================================\n",
+            "Total params: 34,826\n",
+            "Trainable params: 34,826\n",
+            "Non-trainable params: 0\n",
+            "_________________________________________________________________\n"
+          ]
+        }
+      ],
+      "source": [
+        "model = keras.Sequential(\n",
+        "    [\n",
+        "        keras.Input(shape=input_shape),\n",
+        "        layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\"),\n",
+        "        layers.MaxPooling2D(pool_size=(2, 2)),\n",
+        "        layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"),\n",
+        "        layers.MaxPooling2D(pool_size=(2, 2)),\n",
+        "        layers.Flatten(),\n",
+        "        layers.Dropout(0.5),\n",
+        "        layers.Dense(num_classes, activation=\"softmax\"),\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "model.summary()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0hwLCbr-7LP5"
+      },
+      "source": [
+        "## Train the model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Load the TensorBoard notebook extension\n",
+        "%load_ext tensorboard"
+      ],
+      "metadata": {
+        "id": "w_Q7X180AYbB"
+      },
+      "execution_count": 12,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "log_dir = \"logs/fit/\"\n",
+        "tensorboard_callback = keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)"
+      ],
+      "metadata": {
+        "id": "vRhyg5W-AbLU"
+      },
+      "execution_count": 14,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "AEXgbiWZ7LP5",
+        "outputId": "45222492-a30b-4a64-e53f-1a7d5ee3652c"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/5\n",
+            "422/422 [==============================] - 47s 109ms/step - loss: 0.0274 - accuracy: 0.9909 - val_loss: 0.0292 - val_accuracy: 0.9923\n",
+            "Epoch 2/5\n",
+            "422/422 [==============================] - 44s 105ms/step - loss: 0.0273 - accuracy: 0.9907 - val_loss: 0.0280 - val_accuracy: 0.9917\n",
+            "Epoch 3/5\n",
+            "422/422 [==============================] - 43s 102ms/step - loss: 0.0263 - accuracy: 0.9913 - val_loss: 0.0262 - val_accuracy: 0.9937\n",
+            "Epoch 4/5\n",
+            "422/422 [==============================] - 42s 100ms/step - loss: 0.0242 - accuracy: 0.9916 - val_loss: 0.0260 - val_accuracy: 0.9927\n",
+            "Epoch 5/5\n",
+            "422/422 [==============================] - 43s 102ms/step - loss: 0.0242 - accuracy: 0.9917 - val_loss: 0.0311 - val_accuracy: 0.9917\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<keras.callbacks.History at 0x7f802978cb10>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 15
+        }
+      ],
+      "source": [
+        "batch_size = 128\n",
+        "epochs = 5\n",
+        "\n",
+        "model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
+        "\n",
+        "model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1,\n",
+        "          callbacks = [tensorboard_callback])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We will push our model to the Hugging Face Hub with tensorboard logs.\n",
+        "\n",
+        "If you already have access to keras-io organization, you can give \"keras-io/{model-name}\" as the model ID. If not, you can push model to your own account and then carry it to the keras-io organization later. 🥳\n",
+        "\n",
+        "To push your models to the Hub, you need authentication. To authenticate, you can log using notebook_login. You can get your token from https://huggingface.co/settings/tokens 🙌🏻"
+      ],
+      "metadata": {
+        "id": "cIYC7UnFBjHR"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "notebook_login()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 471,
+          "referenced_widgets": [
+            "a3f2334822e7452abaaf93ccc47ba051",
+            "2b99210c02a041fb86b2935e4a1ced6e",
+            "38c57d5de54b45c2aef003db892e38c6",
+            "38aea29e8c654739b8e93935d1ff45fe",
+            "9fc97a48a10b4211adaddf710b20ca71",
+            "6f08c5a0af3745e49a4bd98d89df1c7f",
+            "92fcdadef15d48b3a92e969613076504",
+            "54ac8eb952c14dd1b9585bf97aef046b",
+            "1e8921fab93d496f85a611903e87d9ef",
+            "5430fb89181a46df945835040dfaaa73",
+            "0ca7af69b9e0424e8de5f0bff45e9517",
+            "72ca67223062400283daf47b2d43363a",
+            "5ef27ad680c142c0ba410a93aa991fb7",
+            "b2b3738b36bc4f2b9c30f85afb3c3070",
+            "cd740c81dc2c401ca2c7d163ae1d36ca",
+            "5da678bbd0784cd48c2619aeb7ed62d7",
+            "9c117ad0cd2248a5987a62d1c9e3e10f"
+          ]
+        },
+        "id": "lpPBVGq8_OVV",
+        "outputId": "4b25884d-862b-4b03-a5cb-3f5d1f66d322"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Login successful\n",
+            "Your token has been saved to /root/.huggingface/token\n",
+            "\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n",
+            "You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n",
+            "\n",
+            "git config --global credential.helper store\u001b[0m\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now we can push our model to the Hugging Face Hub 🤩🙌🏻 \n",
+        "The below function will:\n",
+        "\n",
+        "1. create a remote repository on Hugging Face Hub,\n",
+        "2. serialize our model,\n",
+        "3. create a model card including training hyperparameters, model architecture and couple of fields you can fill about model,\n",
+        "4. push the model to the Hub."
+      ],
+      "metadata": {
+        "id": "eLbbU8AnBtqu"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "push_to_hub_keras(model, \"merve/mnist\", log_dir = \"logs/fit/\", tags = [\"image-classification\"])"
+      ],
+      "metadata": {
+        "id": "1RNi4hGKAVUv"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "See pushed model with TensorBoard and model card [here](https://huggingface.co/merve/mnist)."
+      ],
+      "metadata": {
+        "id": "kW6aHvOeC8oJ"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Let's load the model!"
+      ],
+      "metadata": {
+        "id": "XCsztZCtFCdu"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model = from_pretrained_keras(\"merve/mnist\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9Wept7KeFEx7",
+        "outputId": "38f10ece-30bc-45b3-fbc8-e7a1b7eb5a3c"
+      },
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "config.json not found in HuggingFace Hub\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "U_OrylJv7LP6"
+      },
+      "source": [
+        "## Evaluate the trained model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "RVwK3ZHJ7LP6",
+        "outputId": "1adb6f61-2532-4475-cf10-55db0e69d552"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Test loss: 0.022792112082242966\n",
+            "Test accuracy: 0.9919999837875366\n"
+          ]
+        }
+      ],
+      "source": [
+        "score = model.evaluate(x_test, y_test, verbose=0)\n",
+        "print(\"Test loss:\", score[0])\n",
+        "print(\"Test accuracy:\", score[1])"
+      ]
+    }
+  ],
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+      "name": "mnist_convnet",
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diff --git a/merges.txt b/merges.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6038932a2a1f09a66991b1c2adae0d14066fa29e
--- /dev/null
+++ b/merges.txt
@@ -0,0 +1,50001 @@
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diff --git a/model.safetensors b/model.safetensors
new file mode 100644
index 0000000000000000000000000000000000000000..aa567b40e84432c716cfba413ffb596f1f66276a
--- /dev/null
+++ b/model.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1fd14aa6e594aaaee27a96da4e4572c377c57a1c513035f347084de089bcd7b8
+size 966995080
diff --git a/normalizer.json b/normalizer.json
new file mode 100644
index 0000000000000000000000000000000000000000..dd6ae819ad738ac1a546e9f9282ef325c33b9ea0
--- /dev/null
+++ b/normalizer.json
@@ -0,0 +1,1742 @@
+{
+  "accessorise": "accessorize",
+  "accessorised": "accessorized",
+  "accessorises": "accessorizes",
+  "accessorising": "accessorizing",
+  "acclimatisation": "acclimatization",
+  "acclimatise": "acclimatize",
+  "acclimatised": "acclimatized",
+  "acclimatises": "acclimatizes",
+  "acclimatising": "acclimatizing",
+  "accoutrements": "accouterments",
+  "aeon": "eon",
+  "aeons": "eons",
+  "aerogramme": "aerogram",
+  "aerogrammes": "aerograms",
+  "aeroplane": "airplane",
+  "aeroplanes": "airplanes",
+  "aesthete": "esthete",
+  "aesthetes": "esthetes",
+  "aesthetic": "esthetic",
+  "aesthetically": "esthetically",
+  "aesthetics": "esthetics",
+  "aetiology": "etiology",
+  "ageing": "aging",
+  "aggrandisement": "aggrandizement",
+  "agonise": "agonize",
+  "agonised": "agonized",
+  "agonises": "agonizes",
+  "agonising": "agonizing",
+  "agonisingly": "agonizingly",
+  "almanack": "almanac",
+  "almanacks": "almanacs",
+  "aluminium": "aluminum",
+  "amortisable": "amortizable",
+  "amortisation": "amortization",
+  "amortisations": "amortizations",
+  "amortise": "amortize",
+  "amortised": "amortized",
+  "amortises": "amortizes",
+  "amortising": "amortizing",
+  "amphitheatre": "amphitheater",
+  "amphitheatres": "amphitheaters",
+  "anaemia": "anemia",
+  "anaemic": "anemic",
+  "anaesthesia": "anesthesia",
+  "anaesthetic": "anesthetic",
+  "anaesthetics": "anesthetics",
+  "anaesthetise": "anesthetize",
+  "anaesthetised": "anesthetized",
+  "anaesthetises": "anesthetizes",
+  "anaesthetising": "anesthetizing",
+  "anaesthetist": "anesthetist",
+  "anaesthetists": "anesthetists",
+  "anaesthetize": "anesthetize",
+  "anaesthetized": "anesthetized",
+  "anaesthetizes": "anesthetizes",
+  "anaesthetizing": "anesthetizing",
+  "analogue": "analog",
+  "analogues": "analogs",
+  "analyse": "analyze",
+  "analysed": "analyzed",
+  "analyses": "analyzes",
+  "analysing": "analyzing",
+  "anglicise": "anglicize",
+  "anglicised": "anglicized",
+  "anglicises": "anglicizes",
+  "anglicising": "anglicizing",
+  "annualised": "annualized",
+  "antagonise": "antagonize",
+  "antagonised": "antagonized",
+  "antagonises": "antagonizes",
+  "antagonising": "antagonizing",
+  "apologise": "apologize",
+  "apologised": "apologized",
+  "apologises": "apologizes",
+  "apologising": "apologizing",
+  "appal": "appall",
+  "appals": "appalls",
+  "appetiser": "appetizer",
+  "appetisers": "appetizers",
+  "appetising": "appetizing",
+  "appetisingly": "appetizingly",
+  "arbour": "arbor",
+  "arbours": "arbors",
+  "archaeologically": "archeologically",
+  "archaeologist": "archeologist",
+  "archaeologists": "archeologists",
+  "archaeology": "archeology</span>",
+  "archeological": "archaeological",
+  "ardour": "ardor",
+  "armour": "armor",
+  "armoured": "armored",
+  "armourer": "armorer",
+  "armourers": "armorers",
+  "armouries": "armories",
+  "armoury": "armory",
+  "artefact": "artifact",
+  "artefacts": "artifacts",
+  "authorise": "authorize",
+  "authorised": "authorized",
+  "authorises": "authorizes",
+  "authorising": "authorizing",
+  "axe": "ax",
+  "backpedalled": "backpedaled",
+  "backpedalling": "backpedaling",
+  "bannister": "banister",
+  "bannisters": "banisters",
+  "baptise": "baptize",
+  "baptised": "baptized",
+  "baptises": "baptizes",
+  "baptising": "baptizing",
+  "bastardise": "bastardize",
+  "bastardised": "bastardized",
+  "bastardises": "bastardizes",
+  "bastardising": "bastardizing",
+  "battleax": "battleaxe",
+  "baulk": "balk",
+  "baulked": "balked",
+  "baulking": "balking",
+  "baulks": "balks",
+  "bedevilled": "bedeviled",
+  "bedevilling": "bedeviling",
+  "behaviour": "behavior",
+  "behavioural": "behavioral",
+  "behaviourism": "behaviorism",
+  "behaviourist": "behaviorist",
+  "behaviourists": "behaviorists",
+  "behaviours": "behaviors",
+  "behove": "behoove",
+  "behoved": "behooved",
+  "behoves": "behooves",
+  "bejewelled": "bejeweled",
+  "belabour": "belabor",
+  "belaboured": "belabored",
+  "belabouring": "belaboring",
+  "belabours": "belabors",
+  "bevelled": "beveled",
+  "bevvies": "bevies",
+  "bevvy": "bevy",
+  "biassed": "biased",
+  "biassing": "biasing",
+  "bingeing": "binging",
+  "bougainvillaea": "bougainvillea",
+  "bougainvillaeas": "bougainvilleas",
+  "bowdlerise": "bowdlerize",
+  "bowdlerised": "bowdlerized",
+  "bowdlerises": "bowdlerizes",
+  "bowdlerising": "bowdlerizing",
+  "breathalyse": "breathalyze",
+  "breathalysed": "breathalyzed",
+  "breathalyser": "breathalyzer",
+  "breathalysers": "breathalyzers",
+  "breathalyses": "breathalyzes",
+  "breathalysing": "breathalyzing",
+  "brutalise": "brutalize",
+  "brutalised": "brutalized",
+  "brutalises": "brutalizes",
+  "brutalising": "brutalizing",
+  "busses": "buses",
+  "bussing": "busing",
+  "caesarean": "cesarean",
+  "caesareans": "cesareans",
+  "calibre": "caliber",
+  "calibres": "calibers",
+  "calliper": "caliper",
+  "callipers": "calipers",
+  "callisthenics": "calisthenics",
+  "canalise": "canalize",
+  "canalised": "canalized",
+  "canalises": "canalizes",
+  "canalising": "canalizing",
+  "cancelation": "cancellation",
+  "cancelations": "cancellations",
+  "cancelled": "canceled",
+  "cancelling": "canceling",
+  "candour": "candor",
+  "cannibalise": "cannibalize",
+  "cannibalised": "cannibalized",
+  "cannibalises": "cannibalizes",
+  "cannibalising": "cannibalizing",
+  "canonise": "canonize",
+  "canonised": "canonized",
+  "canonises": "canonizes",
+  "canonising": "canonizing",
+  "capitalise": "capitalize",
+  "capitalised": "capitalized",
+  "capitalises": "capitalizes",
+  "capitalising": "capitalizing",
+  "caramelise": "caramelize",
+  "caramelised": "caramelized",
+  "caramelises": "caramelizes",
+  "caramelising": "caramelizing",
+  "carbonise": "carbonize",
+  "carbonised": "carbonized",
+  "carbonises": "carbonizes",
+  "carbonising": "carbonizing",
+  "carolled": "caroled",
+  "carolling": "caroling",
+  "catalogue": "catalog",
+  "catalogued": "cataloged",
+  "catalogues": "catalogs",
+  "cataloguing": "cataloging",
+  "catalyse": "catalyze",
+  "catalysed": "catalyzed",
+  "catalyses": "catalyzes",
+  "catalysing": "catalyzing",
+  "categorise": "categorize",
+  "categorised": "categorized",
+  "categorises": "categorizes",
+  "categorising": "categorizing",
+  "cauterise": "cauterize",
+  "cauterised": "cauterized",
+  "cauterises": "cauterizes",
+  "cauterising": "cauterizing",
+  "cavilled": "caviled",
+  "cavilling": "caviling",
+  "centigramme": "centigram",
+  "centigrammes": "centigrams",
+  "centilitre": "centiliter",
+  "centilitres": "centiliters",
+  "centimetre": "centimeter",
+  "centimetres": "centimeters",
+  "centralise": "centralize",
+  "centralised": "centralized",
+  "centralises": "centralizes",
+  "centralising": "centralizing",
+  "centre": "center",
+  "centred": "centered",
+  "centrefold": "centerfold",
+  "centrefolds": "centerfolds",
+  "centrepiece": "centerpiece",
+  "centrepieces": "centerpieces",
+  "centres": "centers",
+  "channelled": "channeled",
+  "channelling": "channeling",
+  "characterise": "characterize",
+  "characterised": "characterized",
+  "characterises": "characterizes",
+  "characterising": "characterizing",
+  "cheque": "check",
+  "chequebook": "checkbook",
+  "chequebooks": "checkbooks",
+  "chequered": "checkered",
+  "cheques": "checks",
+  "chilli": "chili",
+  "chimaera": "chimera",
+  "chimaeras": "chimeras",
+  "chiselled": "chiseled",
+  "chiselling": "chiseling",
+  "circularise": "circularize",
+  "circularised": "circularized",
+  "circularises": "circularizes",
+  "circularising": "circularizing",
+  "civilise": "civilize",
+  "civilised": "civilized",
+  "civilises": "civilizes",
+  "civilising": "civilizing",
+  "clamour": "clamor",
+  "clamoured": "clamored",
+  "clamouring": "clamoring",
+  "clamours": "clamors",
+  "clangour": "clangor",
+  "clarinettist": "clarinetist",
+  "clarinettists": "clarinetists",
+  "collectivise": "collectivize",
+  "collectivised": "collectivized",
+  "collectivises": "collectivizes",
+  "collectivising": "collectivizing",
+  "colonisation": "colonization",
+  "colonise": "colonize",
+  "colonised": "colonized",
+  "coloniser": "colonizer",
+  "colonisers": "colonizers",
+  "colonises": "colonizes",
+  "colonising": "colonizing",
+  "colour": "color",
+  "colourant": "colorant",
+  "colourants": "colorants",
+  "coloured": "colored",
+  "coloureds": "coloreds",
+  "colourful": "colorful",
+  "colourfully": "colorfully",
+  "colouring": "coloring",
+  "colourize": "colorize",
+  "colourized": "colorized",
+  "colourizes": "colorizes",
+  "colourizing": "colorizing",
+  "colourless": "colorless",
+  "colours": "colors",
+  "commercialise": "commercialize",
+  "commercialised": "commercialized",
+  "commercialises": "commercializes",
+  "commercialising": "commercializing",
+  "compartmentalise": "compartmentalize",
+  "compartmentalised": "compartmentalized",
+  "compartmentalises": "compartmentalizes",
+  "compartmentalising": "compartmentalizing",
+  "computerise": "computerize",
+  "computerised": "computerized",
+  "computerises": "computerizes",
+  "computerising": "computerizing",
+  "conceptualise": "conceptualize",
+  "conceptualised": "conceptualized",
+  "conceptualises": "conceptualizes",
+  "conceptualising": "conceptualizing",
+  "connexion": "connection",
+  "connexions": "connections",
+  "contextualise": "contextualize",
+  "contextualised": "contextualized",
+  "contextualises": "contextualizes",
+  "contextualising": "contextualizing",
+  "cosier": "cozier",
+  "cosies": "cozies",
+  "cosiest": "coziest",
+  "cosily": "cozily",
+  "cosiness": "coziness",
+  "cosy": "cozy",
+  "councillor": "councilor",
+  "councillors": "councilors",
+  "counselled": "counseled",
+  "counselling": "counseling",
+  "counsellor": "counselor",
+  "counsellors": "counselors",
+  "crenelated": "crenellated",
+  "criminalise": "criminalize",
+  "criminalised": "criminalized",
+  "criminalises": "criminalizes",
+  "criminalising": "criminalizing",
+  "criticise": "criticize",
+  "criticised": "criticized",
+  "criticises": "criticizes",
+  "criticising": "criticizing",
+  "crueller": "crueler",
+  "cruellest": "cruelest",
+  "crystallisation": "crystallization",
+  "crystallise": "crystallize",
+  "crystallised": "crystallized",
+  "crystallises": "crystallizes",
+  "crystallising": "crystallizing",
+  "cudgelled": "cudgeled",
+  "cudgelling": "cudgeling",
+  "customise": "customize",
+  "customised": "customized",
+  "customises": "customizes",
+  "customising": "customizing",
+  "cypher": "cipher",
+  "cyphers": "ciphers",
+  "decentralisation": "decentralization",
+  "decentralise": "decentralize",
+  "decentralised": "decentralized",
+  "decentralises": "decentralizes",
+  "decentralising": "decentralizing",
+  "decriminalisation": "decriminalization",
+  "decriminalise": "decriminalize",
+  "decriminalised": "decriminalized",
+  "decriminalises": "decriminalizes",
+  "decriminalising": "decriminalizing",
+  "defence": "defense",
+  "defenceless": "defenseless",
+  "defences": "defenses",
+  "dehumanisation": "dehumanization",
+  "dehumanise": "dehumanize",
+  "dehumanised": "dehumanized",
+  "dehumanises": "dehumanizes",
+  "dehumanising": "dehumanizing",
+  "demeanour": "demeanor",
+  "demilitarisation": "demilitarization",
+  "demilitarise": "demilitarize",
+  "demilitarised": "demilitarized",
+  "demilitarises": "demilitarizes",
+  "demilitarising": "demilitarizing",
+  "demobilisation": "demobilization",
+  "demobilise": "demobilize",
+  "demobilised": "demobilized",
+  "demobilises": "demobilizes",
+  "demobilising": "demobilizing",
+  "democratisation": "democratization",
+  "democratise": "democratize",
+  "democratised": "democratized",
+  "democratises": "democratizes",
+  "democratising": "democratizing",
+  "demonise": "demonize",
+  "demonised": "demonized",
+  "demonises": "demonizes",
+  "demonising": "demonizing",
+  "demoralisation": "demoralization",
+  "demoralise": "demoralize",
+  "demoralised": "demoralized",
+  "demoralises": "demoralizes",
+  "demoralising": "demoralizing",
+  "denationalisation": "denationalization",
+  "denationalise": "denationalize",
+  "denationalised": "denationalized",
+  "denationalises": "denationalizes",
+  "denationalising": "denationalizing",
+  "deodorise": "deodorize",
+  "deodorised": "deodorized",
+  "deodorises": "deodorizes",
+  "deodorising": "deodorizing",
+  "depersonalise": "depersonalize",
+  "depersonalised": "depersonalized",
+  "depersonalises": "depersonalizes",
+  "depersonalising": "depersonalizing",
+  "deputise": "deputize",
+  "deputised": "deputized",
+  "deputises": "deputizes",
+  "deputising": "deputizing",
+  "desensitisation": "desensitization",
+  "desensitise": "desensitize",
+  "desensitised": "desensitized",
+  "desensitises": "desensitizes",
+  "desensitising": "desensitizing",
+  "destabilisation": "destabilization",
+  "destabilise": "destabilize",
+  "destabilised": "destabilized",
+  "destabilises": "destabilizes",
+  "destabilising": "destabilizing",
+  "dialled": "dialed",
+  "dialling": "dialing",
+  "dialogue": "dialog",
+  "dialogues": "dialogs",
+  "diarrhoea": "diarrhea",
+  "digitise": "digitize",
+  "digitised": "digitized",
+  "digitises": "digitizes",
+  "digitising": "digitizing",
+  "disc": "disk",
+  "discolour": "discolor",
+  "discoloured": "discolored",
+  "discolouring": "discoloring",
+  "discolours": "discolors",
+  "discs": "disks",
+  "disembowelled": "disemboweled",
+  "disembowelling": "disemboweling",
+  "disfavour": "disfavor",
+  "dishevelled": "disheveled",
+  "dishonour": "dishonor",
+  "dishonourable": "dishonorable",
+  "dishonourably": "dishonorably",
+  "dishonoured": "dishonored",
+  "dishonouring": "dishonoring",
+  "dishonours": "dishonors",
+  "disorganisation": "disorganization",
+  "disorganised": "disorganized",
+  "distil": "distill",
+  "distils": "distills",
+  "dramatisation": "dramatization",
+  "dramatisations": "dramatizations",
+  "dramatise": "dramatize",
+  "dramatised": "dramatized",
+  "dramatises": "dramatizes",
+  "dramatising": "dramatizing",
+  "draught": "draft",
+  "draughtboard": "draftboard",
+  "draughtboards": "draftboards",
+  "draughtier": "draftier",
+  "draughtiest": "draftiest",
+  "draughts": "drafts",
+  "draughtsman": "draftsman",
+  "draughtsmanship": "draftsmanship",
+  "draughtsmen": "draftsmen",
+  "draughtswoman": "draftswoman",
+  "draughtswomen": "draftswomen",
+  "draughty": "drafty",
+  "drivelled": "driveled",
+  "drivelling": "driveling",
+  "duelled": "dueled",
+  "duelling": "dueling",
+  "economise": "economize",
+  "economised": "economized",
+  "economises": "economizes",
+  "economising": "economizing",
+  "editorialise": "editorialize",
+  "editorialised": "editorialized",
+  "editorialises": "editorializes",
+  "editorialising": "editorializing",
+  "edoema": "edema",
+  "empathise": "empathize",
+  "empathised": "empathized",
+  "empathises": "empathizes",
+  "empathising": "empathizing",
+  "emphasise": "emphasize",
+  "emphasised": "emphasized",
+  "emphasises": "emphasizes",
+  "emphasising": "emphasizing",
+  "enamelled": "enameled",
+  "enamelling": "enameling",
+  "enamoured": "enamored",
+  "encyclopaedia": "encyclopedia",
+  "encyclopaedias": "encyclopedias",
+  "encyclopaedic": "encyclopedic",
+  "endeavour": "endeavor",
+  "endeavoured": "endeavored",
+  "endeavouring": "endeavoring",
+  "endeavours": "endeavors",
+  "energise": "energize",
+  "energised": "energized",
+  "energises": "energizes",
+  "energising": "energizing",
+  "enrol": "enroll",
+  "enrols": "enrolls",
+  "enthral": "enthrall",
+  "enthrals": "enthralls",
+  "epaulette": "epaulet",
+  "epaulettes": "epaulets",
+  "epicentre": "epicenter",
+  "epicentres": "epicenters",
+  "epilogue": "epilog",
+  "epilogues": "epilogs",
+  "epitomise": "epitomize",
+  "epitomised": "epitomized",
+  "epitomises": "epitomizes",
+  "epitomising": "epitomizing",
+  "equalisation": "equalization",
+  "equalise": "equalize",
+  "equalised": "equalized",
+  "equaliser": "equalizer",
+  "equalisers": "equalizers",
+  "equalises": "equalizes",
+  "equalising": "equalizing",
+  "eulogise": "eulogize",
+  "eulogised": "eulogized",
+  "eulogises": "eulogizes",
+  "eulogising": "eulogizing",
+  "evangelise": "evangelize",
+  "evangelised": "evangelized",
+  "evangelises": "evangelizes",
+  "evangelising": "evangelizing",
+  "exorcise": "exorcize",
+  "exorcised": "exorcized",
+  "exorcises": "exorcizes",
+  "exorcising": "exorcizing",
+  "extemporisation": "extemporization",
+  "extemporise": "extemporize",
+  "extemporised": "extemporized",
+  "extemporises": "extemporizes",
+  "extemporising": "extemporizing",
+  "externalisation": "externalization",
+  "externalisations": "externalizations",
+  "externalise": "externalize",
+  "externalised": "externalized",
+  "externalises": "externalizes",
+  "externalising": "externalizing",
+  "factorise": "factorize",
+  "factorised": "factorized",
+  "factorises": "factorizes",
+  "factorising": "factorizing",
+  "faecal": "fecal",
+  "faeces": "feces",
+  "familiarisation": "familiarization",
+  "familiarise": "familiarize",
+  "familiarised": "familiarized",
+  "familiarises": "familiarizes",
+  "familiarising": "familiarizing",
+  "fantasise": "fantasize",
+  "fantasised": "fantasized",
+  "fantasises": "fantasizes",
+  "fantasising": "fantasizing",
+  "favour": "favor",
+  "favourable": "favorable",
+  "favourably": "favorably",
+  "favoured": "favored",
+  "favouring": "favoring",
+  "favourite": "favorite",
+  "favourites": "favorites",
+  "favouritism": "favoritism",
+  "favours": "favors",
+  "feminise": "feminize",
+  "feminised": "feminized",
+  "feminises": "feminizes",
+  "feminising": "feminizing",
+  "fertilisation": "fertilization",
+  "fertilise": "fertilize",
+  "fertilised": "fertilized",
+  "fertiliser": "fertilizer",
+  "fertilisers": "fertilizers",
+  "fertilises": "fertilizes",
+  "fertilising": "fertilizing",
+  "fervour": "fervor",
+  "fibre": "fiber",
+  "fibreglass": "fiberglass",
+  "fibres": "fibers",
+  "fictionalisation": "fictionalization",
+  "fictionalisations": "fictionalizations",
+  "fictionalise": "fictionalize",
+  "fictionalised": "fictionalized",
+  "fictionalises": "fictionalizes",
+  "fictionalising": "fictionalizing",
+  "fillet": "filet",
+  "filleted": "fileted",
+  "filleting": "fileting",
+  "fillets": "filets",
+  "finalisation": "finalization",
+  "finalise": "finalize",
+  "finalised": "finalized",
+  "finalises": "finalizes",
+  "finalising": "finalizing",
+  "flautist": "flutist",
+  "flautists": "flutists",
+  "flavour": "flavor",
+  "flavoured": "flavored",
+  "flavouring": "flavoring",
+  "flavourings": "flavorings",
+  "flavourless": "flavorless",
+  "flavours": "flavors",
+  "flavoursome": "flavorsome",
+  "flyer / flier": "flier / flyer",
+  "foetal": "fetal",
+  "foetid": "fetid",
+  "foetus": "fetus",
+  "foetuses": "fetuses",
+  "formalisation": "formalization",
+  "formalise": "formalize",
+  "formalised": "formalized",
+  "formalises": "formalizes",
+  "formalising": "formalizing",
+  "fossilisation": "fossilization",
+  "fossilise": "fossilize",
+  "fossilised": "fossilized",
+  "fossilises": "fossilizes",
+  "fossilising": "fossilizing",
+  "fraternisation": "fraternization",
+  "fraternise": "fraternize",
+  "fraternised": "fraternized",
+  "fraternises": "fraternizes",
+  "fraternising": "fraternizing",
+  "fulfil": "fulfill",
+  "fulfilment": "fulfillment",
+  "fulfils": "fulfills",
+  "funnelled": "funneled",
+  "funnelling": "funneling",
+  "gage": "gauge",
+  "gaged": "gauged",
+  "gages": "gauges",
+  "gaging": "gauging",
+  "galvanise": "galvanize",
+  "galvanised": "galvanized",
+  "galvanises": "galvanizes",
+  "galvanising": "galvanizing",
+  "gambolled": "gamboled",
+  "gambolling": "gamboling",
+  "gaol": "jail",
+  "gaolbird": "jailbird",
+  "gaolbirds": "jailbirds",
+  "gaolbreak": "jailbreak",
+  "gaolbreaks": "jailbreaks",
+  "gaoled": "jailed",
+  "gaoler": "jailer",
+  "gaolers": "jailers",
+  "gaoling": "jailing",
+  "gaols": "jails",
+  "gasses": "gases",
+  "generalisation": "generalization",
+  "generalisations": "generalizations",
+  "generalise": "generalize",
+  "generalised": "generalized",
+  "generalises": "generalizes",
+  "generalising": "generalizing",
+  "ghettoise": "ghettoize",
+  "ghettoised": "ghettoized",
+  "ghettoises": "ghettoizes",
+  "ghettoising": "ghettoizing",
+  "gipsies": "gypsies",
+  "glamor": "glamour",
+  "glamorise": "glamorize",
+  "glamorised": "glamorized",
+  "glamorises": "glamorizes",
+  "glamorising": "glamorizing",
+  "globalisation": "globalization",
+  "globalise": "globalize",
+  "globalised": "globalized",
+  "globalises": "globalizes",
+  "globalising": "globalizing",
+  "glueing": "gluing",
+  "goitre": "goiter",
+  "goitres": "goiters",
+  "gonorrhoea": "gonorrhea",
+  "gramme": "gram",
+  "grammes": "grams",
+  "gravelled": "graveled",
+  "grey": "gray",
+  "greyed": "grayed",
+  "greying": "graying",
+  "greyish": "grayish",
+  "greyness": "grayness",
+  "greys": "grays",
+  "grovelled": "groveled",
+  "grovelling": "groveling",
+  "groyne": "groin",
+  "groynes": "groins",
+  "gruelling": "grueling",
+  "gruellingly": "gruelingly",
+  "gryphon": "griffin",
+  "gryphons": "griffins",
+  "gynaecological": "gynecological",
+  "gynaecologist": "gynecologist",
+  "gynaecologists": "gynecologists",
+  "gynaecology": "gynecology",
+  "haematological": "hematological",
+  "haematologist": "hematologist",
+  "haematologists": "hematologists",
+  "haematology": "hematology",
+  "haemoglobin": "hemoglobin",
+  "haemophilia": "hemophilia",
+  "haemophiliac": "hemophiliac",
+  "haemophiliacs": "hemophiliacs",
+  "haemorrhage": "hemorrhage",
+  "haemorrhaged": "hemorrhaged",
+  "haemorrhages": "hemorrhages",
+  "haemorrhaging": "hemorrhaging",
+  "haemorrhoids": "hemorrhoids",
+  "harbour": "harbor",
+  "harboured": "harbored",
+  "harbouring": "harboring",
+  "harbours": "harbors",
+  "harmonisation": "harmonization",
+  "harmonise": "harmonize",
+  "harmonised": "harmonized",
+  "harmonises": "harmonizes",
+  "harmonising": "harmonizing",
+  "homoeopath": "homeopath",
+  "homoeopathic": "homeopathic",
+  "homoeopaths": "homeopaths",
+  "homoeopathy": "homeopathy",
+  "homogenise": "homogenize",
+  "homogenised": "homogenized",
+  "homogenises": "homogenizes",
+  "homogenising": "homogenizing",
+  "honour": "honor",
+  "honourable": "honorable",
+  "honourably": "honorably",
+  "honoured": "honored",
+  "honouring": "honoring",
+  "honours": "honors",
+  "hospitalisation": "hospitalization",
+  "hospitalise": "hospitalize",
+  "hospitalised": "hospitalized",
+  "hospitalises": "hospitalizes",
+  "hospitalising": "hospitalizing",
+  "humanise": "humanize",
+  "humanised": "humanized",
+  "humanises": "humanizes",
+  "humanising": "humanizing",
+  "humour": "humor",
+  "humoured": "humored",
+  "humouring": "humoring",
+  "humourless": "humorless",
+  "humours": "humors",
+  "hybridise": "hybridize",
+  "hybridised": "hybridized",
+  "hybridises": "hybridizes",
+  "hybridising": "hybridizing",
+  "hypnotise": "hypnotize",
+  "hypnotised": "hypnotized",
+  "hypnotises": "hypnotizes",
+  "hypnotising": "hypnotizing",
+  "hypothesise": "hypothesize",
+  "hypothesised": "hypothesized",
+  "hypothesises": "hypothesizes",
+  "hypothesising": "hypothesizing",
+  "idealisation": "idealization",
+  "idealise": "idealize",
+  "idealised": "idealized",
+  "idealises": "idealizes",
+  "idealising": "idealizing",
+  "idolise": "idolize",
+  "idolised": "idolized",
+  "idolises": "idolizes",
+  "idolising": "idolizing",
+  "immobilisation": "immobilization",
+  "immobilise": "immobilize",
+  "immobilised": "immobilized",
+  "immobiliser": "immobilizer",
+  "immobilisers": "immobilizers",
+  "immobilises": "immobilizes",
+  "immobilising": "immobilizing",
+  "immortalise": "immortalize",
+  "immortalised": "immortalized",
+  "immortalises": "immortalizes",
+  "immortalising": "immortalizing",
+  "immunisation": "immunization",
+  "immunise": "immunize",
+  "immunised": "immunized",
+  "immunises": "immunizes",
+  "immunising": "immunizing",
+  "impanelled": "impaneled",
+  "impanelling": "impaneling",
+  "imperilled": "imperiled",
+  "imperilling": "imperiling",
+  "individualise": "individualize",
+  "individualised": "individualized",
+  "individualises": "individualizes",
+  "individualising": "individualizing",
+  "industrialise": "industrialize",
+  "industrialised": "industrialized",
+  "industrialises": "industrializes",
+  "industrialising": "industrializing",
+  "inflexion": "inflection",
+  "inflexions": "inflections",
+  "initialise": "initialize",
+  "initialised": "initialized",
+  "initialises": "initializes",
+  "initialising": "initializing",
+  "initialled": "initialed",
+  "initialling": "initialing",
+  "instal": "install",
+  "instalment": "installment",
+  "instalments": "installments",
+  "instals": "installs",
+  "instil": "instill",
+  "instils": "instills",
+  "institutionalisation": "institutionalization",
+  "institutionalise": "institutionalize",
+  "institutionalised": "institutionalized",
+  "institutionalises": "institutionalizes",
+  "institutionalising": "institutionalizing",
+  "intellectualise": "intellectualize",
+  "intellectualised": "intellectualized",
+  "intellectualises": "intellectualizes",
+  "intellectualising": "intellectualizing",
+  "internalisation": "internalization",
+  "internalise": "internalize",
+  "internalised": "internalized",
+  "internalises": "internalizes",
+  "internalising": "internalizing",
+  "internationalisation": "internationalization",
+  "internationalise": "internationalize",
+  "internationalised": "internationalized",
+  "internationalises": "internationalizes",
+  "internationalising": "internationalizing",
+  "ionisation": "ionization",
+  "ionise": "ionize",
+  "ionised": "ionized",
+  "ioniser": "ionizer",
+  "ionisers": "ionizers",
+  "ionises": "ionizes",
+  "ionising": "ionizing",
+  "italicise": "italicize",
+  "italicised": "italicized",
+  "italicises": "italicizes",
+  "italicising": "italicizing",
+  "itemise": "itemize",
+  "itemised": "itemized",
+  "itemises": "itemizes",
+  "itemising": "itemizing",
+  "jeopardise": "jeopardize",
+  "jeopardised": "jeopardized",
+  "jeopardises": "jeopardizes",
+  "jeopardising": "jeopardizing",
+  "jewelled": "jeweled",
+  "jeweller": "jeweler",
+  "jewellers": "jewelers",
+  "jewellery": "jewelry",
+  "judgement": "judgment",
+  "kilogramme": "kilogram",
+  "kilogrammes": "kilograms",
+  "kilometre": "kilometer",
+  "kilometres": "kilometers",
+  "labelled": "labeled",
+  "labelling": "labeling",
+  "labour": "labor",
+  "laboured": "labored",
+  "labourer": "laborer",
+  "labourers": "laborers",
+  "labouring": "laboring",
+  "labours": "labors",
+  "lacklustre": "lackluster",
+  "legalisation": "legalization",
+  "legalise": "legalize",
+  "legalised": "legalized",
+  "legalises": "legalizes",
+  "legalising": "legalizing",
+  "legitimise": "legitimize",
+  "legitimised": "legitimized",
+  "legitimises": "legitimizes",
+  "legitimising": "legitimizing",
+  "leukaemia": "leukemia",
+  "levelled": "leveled",
+  "leveller": "leveler",
+  "levellers": "levelers",
+  "levelling": "leveling",
+  "libelled": "libeled",
+  "libelling": "libeling",
+  "libellous": "libelous",
+  "liberalisation": "liberalization",
+  "liberalise": "liberalize",
+  "liberalised": "liberalized",
+  "liberalises": "liberalizes",
+  "liberalising": "liberalizing",
+  "licence": "license",
+  "licenced": "licensed",
+  "licences": "licenses",
+  "licencing": "licensing",
+  "likeable": "likable",
+  "lionisation": "lionization",
+  "lionise": "lionize",
+  "lionised": "lionized",
+  "lionises": "lionizes",
+  "lionising": "lionizing",
+  "liquidise": "liquidize",
+  "liquidised": "liquidized",
+  "liquidiser": "liquidizer",
+  "liquidisers": "liquidizers",
+  "liquidises": "liquidizes",
+  "liquidising": "liquidizing",
+  "litre": "liter",
+  "litres": "liters",
+  "localise": "localize",
+  "localised": "localized",
+  "localises": "localizes",
+  "localising": "localizing",
+  "louvre": "louver",
+  "louvred": "louvered",
+  "louvres": "louvers",
+  "lustre": "luster",
+  "magnetise": "magnetize",
+  "magnetised": "magnetized",
+  "magnetises": "magnetizes",
+  "magnetising": "magnetizing",
+  "manoeuvrability": "maneuverability",
+  "manoeuvrable": "maneuverable",
+  "manoeuvre": "maneuver",
+  "manoeuvred": "maneuvered",
+  "manoeuvres": "maneuvers",
+  "manoeuvring": "maneuvering",
+  "manoeuvrings": "maneuverings",
+  "marginalisation": "marginalization",
+  "marginalise": "marginalize",
+  "marginalised": "marginalized",
+  "marginalises": "marginalizes",
+  "marginalising": "marginalizing",
+  "marshalled": "marshaled",
+  "marshalling": "marshaling",
+  "marvelled": "marveled",
+  "marvelling": "marveling",
+  "marvellous": "marvelous",
+  "marvellously": "marvelously",
+  "materialisation": "materialization",
+  "materialise": "materialize",
+  "materialised": "materialized",
+  "materialises": "materializes",
+  "materialising": "materializing",
+  "maximisation": "maximization",
+  "maximise": "maximize",
+  "maximised": "maximized",
+  "maximises": "maximizes",
+  "maximising": "maximizing",
+  "meagre": "meager",
+  "mechanisation": "mechanization",
+  "mechanise": "mechanize",
+  "mechanised": "mechanized",
+  "mechanises": "mechanizes",
+  "mechanising": "mechanizing",
+  "mediaeval": "medieval",
+  "memorialise": "memorialize",
+  "memorialised": "memorialized",
+  "memorialises": "memorializes",
+  "memorialising": "memorializing",
+  "memorise": "memorize",
+  "memorised": "memorized",
+  "memorises": "memorizes",
+  "memorising": "memorizing",
+  "mesmerise": "mesmerize",
+  "mesmerised": "mesmerized",
+  "mesmerises": "mesmerizes",
+  "mesmerising": "mesmerizing",
+  "metabolise": "metabolize",
+  "metabolised": "metabolized",
+  "metabolises": "metabolizes",
+  "metabolising": "metabolizing",
+  "metre": "meter",
+  "metres": "meters",
+  "mhm": "hmm",
+  "micrometre": "micrometer",
+  "micrometres": "micrometers",
+  "militarise": "militarize",
+  "militarised": "militarized",
+  "militarises": "militarizes",
+  "militarising": "militarizing",
+  "milligramme": "milligram",
+  "milligrammes": "milligrams",
+  "millilitre": "milliliter",
+  "millilitres": "milliliters",
+  "millimetre": "millimeter",
+  "millimetres": "millimeters",
+  "miniaturisation": "miniaturization",
+  "miniaturise": "miniaturize",
+  "miniaturised": "miniaturized",
+  "miniaturises": "miniaturizes",
+  "miniaturising": "miniaturizing",
+  "minibusses": "minibuses",
+  "minimise": "minimize",
+  "minimised": "minimized",
+  "minimises": "minimizes",
+  "minimising": "minimizing",
+  "misbehaviour": "misbehavior",
+  "misdemeanour": "misdemeanor",
+  "misdemeanours": "misdemeanors",
+  "misspelt": "misspelled",
+  "mitre": "miter",
+  "mitres": "miters",
+  "mm": "hmm",
+  "mmm": "hmm",
+  "mobilisation": "mobilization",
+  "mobilise": "mobilize",
+  "mobilised": "mobilized",
+  "mobilises": "mobilizes",
+  "mobilising": "mobilizing",
+  "modelled": "modeled",
+  "modeller": "modeler",
+  "modellers": "modelers",
+  "modelling": "modeling",
+  "modernise": "modernize",
+  "modernised": "modernized",
+  "modernises": "modernizes",
+  "modernising": "modernizing",
+  "moisturise": "moisturize",
+  "moisturised": "moisturized",
+  "moisturiser": "moisturizer",
+  "moisturisers": "moisturizers",
+  "moisturises": "moisturizes",
+  "moisturising": "moisturizing",
+  "monologue": "monolog",
+  "monologues": "monologs",
+  "monopolisation": "monopolization",
+  "monopolise": "monopolize",
+  "monopolised": "monopolized",
+  "monopolises": "monopolizes",
+  "monopolising": "monopolizing",
+  "moralise": "moralize",
+  "moralised": "moralized",
+  "moralises": "moralizes",
+  "moralising": "moralizing",
+  "motorised": "motorized",
+  "mould": "mold",
+  "moulded": "molded",
+  "moulder": "molder",
+  "mouldered": "moldered",
+  "mouldering": "moldering",
+  "moulders": "molders",
+  "mouldier": "moldier",
+  "mouldiest": "moldiest",
+  "moulding": "molding",
+  "mouldings": "moldings",
+  "moulds": "molds",
+  "mouldy": "moldy",
+  "moult": "molt",
+  "moulted": "molted",
+  "moulting": "molting",
+  "moults": "molts",
+  "moustache": "mustache",
+  "moustached": "mustached",
+  "moustaches": "mustaches",
+  "moustachioed": "mustachioed",
+  "multicoloured": "multicolored",
+  "nationalisation": "nationalization",
+  "nationalisations": "nationalizations",
+  "nationalise": "nationalize",
+  "nationalised": "nationalized",
+  "nationalises": "nationalizes",
+  "nationalising": "nationalizing",
+  "naturalisation": "naturalization",
+  "naturalise": "naturalize",
+  "naturalised": "naturalized",
+  "naturalises": "naturalizes",
+  "naturalising": "naturalizing",
+  "neighbour": "neighbor",
+  "neighbourhood": "neighborhood",
+  "neighbourhoods": "neighborhoods",
+  "neighbouring": "neighboring",
+  "neighbourliness": "neighborliness",
+  "neighbourly": "neighborly",
+  "neighbours": "neighbors",
+  "neutralisation": "neutralization",
+  "neutralise": "neutralize",
+  "neutralised": "neutralized",
+  "neutralises": "neutralizes",
+  "neutralising": "neutralizing",
+  "normalisation": "normalization",
+  "normalise": "normalize",
+  "normalised": "normalized",
+  "normalises": "normalizes",
+  "normalising": "normalizing",
+  "odour": "odor",
+  "odourless": "odorless",
+  "odours": "odors",
+  "oesophagus": "esophagus",
+  "oesophaguses": "esophaguses",
+  "oestrogen": "estrogen",
+  "offence": "offense",
+  "offences": "offenses",
+  "omelette": "omelet",
+  "omelettes": "omelets",
+  "optimise": "optimize",
+  "optimised": "optimized",
+  "optimises": "optimizes",
+  "optimising": "optimizing",
+  "organisation": "organization",
+  "organisational": "organizational",
+  "organisations": "organizations",
+  "organise": "organize",
+  "organised": "organized",
+  "organiser": "organizer",
+  "organisers": "organizers",
+  "organises": "organizes",
+  "organising": "organizing",
+  "orthopaedic": "orthopedic",
+  "orthopaedics": "orthopedics",
+  "ostracise": "ostracize",
+  "ostracised": "ostracized",
+  "ostracises": "ostracizes",
+  "ostracising": "ostracizing",
+  "outmanoeuvre": "outmaneuver",
+  "outmanoeuvred": "outmaneuvered",
+  "outmanoeuvres": "outmaneuvers",
+  "outmanoeuvring": "outmaneuvering",
+  "overemphasise": "overemphasize",
+  "overemphasised": "overemphasized",
+  "overemphasises": "overemphasizes",
+  "overemphasising": "overemphasizing",
+  "oxidisation": "oxidization",
+  "oxidise": "oxidize",
+  "oxidised": "oxidized",
+  "oxidises": "oxidizes",
+  "oxidising": "oxidizing",
+  "paederast": "pederast",
+  "paederasts": "pederasts",
+  "paediatric": "pediatric",
+  "paediatrician": "pediatrician",
+  "paediatricians": "pediatricians",
+  "paediatrics": "pediatrics",
+  "paedophile": "pedophile",
+  "paedophiles": "pedophiles",
+  "paedophilia": "pedophilia",
+  "palaeolithic": "paleolithic",
+  "palaeontologist": "paleontologist",
+  "palaeontologists": "paleontologists",
+  "palaeontology": "paleontology",
+  "panelled": "paneled",
+  "panelling": "paneling",
+  "panellist": "panelist",
+  "panellists": "panelists",
+  "paralyse": "paralyze",
+  "paralysed": "paralyzed",
+  "paralyses": "paralyzes",
+  "paralysing": "paralyzing",
+  "parcelled": "parceled",
+  "parcelling": "parceling",
+  "parlour": "parlor",
+  "parlours": "parlors",
+  "particularise": "particularize",
+  "particularised": "particularized",
+  "particularises": "particularizes",
+  "particularising": "particularizing",
+  "passivisation": "passivization",
+  "passivise": "passivize",
+  "passivised": "passivized",
+  "passivises": "passivizes",
+  "passivising": "passivizing",
+  "pasteurisation": "pasteurization",
+  "pasteurise": "pasteurize",
+  "pasteurised": "pasteurized",
+  "pasteurises": "pasteurizes",
+  "pasteurising": "pasteurizing",
+  "patronise": "patronize",
+  "patronised": "patronized",
+  "patronises": "patronizes",
+  "patronising": "patronizing",
+  "patronisingly": "patronizingly",
+  "pedalled": "pedaled",
+  "pedalling": "pedaling",
+  "pedestrianisation": "pedestrianization",
+  "pedestrianise": "pedestrianize",
+  "pedestrianised": "pedestrianized",
+  "pedestrianises": "pedestrianizes",
+  "pedestrianising": "pedestrianizing",
+  "penalise": "penalize",
+  "penalised": "penalized",
+  "penalises": "penalizes",
+  "penalising": "penalizing",
+  "pencilled": "penciled",
+  "pencilling": "penciling",
+  "personalise": "personalize",
+  "personalised": "personalized",
+  "personalises": "personalizes",
+  "personalising": "personalizing",
+  "pharmacopoeia": "pharmacopeia",
+  "pharmacopoeias": "pharmacopeias",
+  "philosophise": "philosophize",
+  "philosophised": "philosophized",
+  "philosophises": "philosophizes",
+  "philosophising": "philosophizing",
+  "philtre": "filter",
+  "philtres": "filters",
+  "phoney": "phony",
+  "plagiarise": "plagiarize",
+  "plagiarised": "plagiarized",
+  "plagiarises": "plagiarizes",
+  "plagiarising": "plagiarizing",
+  "plough": "plow",
+  "ploughed": "plowed",
+  "ploughing": "plowing",
+  "ploughman": "plowman",
+  "ploughmen": "plowmen",
+  "ploughs": "plows",
+  "ploughshare": "plowshare",
+  "ploughshares": "plowshares",
+  "polarisation": "polarization",
+  "polarise": "polarize",
+  "polarised": "polarized",
+  "polarises": "polarizes",
+  "polarising": "polarizing",
+  "politicisation": "politicization",
+  "politicise": "politicize",
+  "politicised": "politicized",
+  "politicises": "politicizes",
+  "politicising": "politicizing",
+  "popularisation": "popularization",
+  "popularise": "popularize",
+  "popularised": "popularized",
+  "popularises": "popularizes",
+  "popularising": "popularizing",
+  "pouffe": "pouf",
+  "pouffes": "poufs",
+  "practise": "practice",
+  "practised": "practiced",
+  "practises": "practices",
+  "practising": "practicing",
+  "praesidium": "presidium",
+  "praesidiums": "presidiums",
+  "pressurisation": "pressurization",
+  "pressurise": "pressurize",
+  "pressurised": "pressurized",
+  "pressurises": "pressurizes",
+  "pressurising": "pressurizing",
+  "pretence": "pretense",
+  "pretences": "pretenses",
+  "primaeval": "primeval",
+  "prioritisation": "prioritization",
+  "prioritise": "prioritize",
+  "prioritised": "prioritized",
+  "prioritises": "prioritizes",
+  "prioritising": "prioritizing",
+  "privatisation": "privatization",
+  "privatisations": "privatizations",
+  "privatise": "privatize",
+  "privatised": "privatized",
+  "privatises": "privatizes",
+  "privatising": "privatizing",
+  "professionalisation": "professionalization",
+  "professionalise": "professionalize",
+  "professionalised": "professionalized",
+  "professionalises": "professionalizes",
+  "professionalising": "professionalizing",
+  "programme": "program",
+  "programmes": "programs",
+  "prologue": "prolog",
+  "prologues": "prologs",
+  "propagandise": "propagandize",
+  "propagandised": "propagandized",
+  "propagandises": "propagandizes",
+  "propagandising": "propagandizing",
+  "proselytise": "proselytize",
+  "proselytised": "proselytized",
+  "proselytiser": "proselytizer",
+  "proselytisers": "proselytizers",
+  "proselytises": "proselytizes",
+  "proselytising": "proselytizing",
+  "psychoanalyse": "psychoanalyze",
+  "psychoanalysed": "psychoanalyzed",
+  "psychoanalyses": "psychoanalyzes",
+  "psychoanalysing": "psychoanalyzing",
+  "publicise": "publicize",
+  "publicised": "publicized",
+  "publicises": "publicizes",
+  "publicising": "publicizing",
+  "pulverisation": "pulverization",
+  "pulverise": "pulverize",
+  "pulverised": "pulverized",
+  "pulverises": "pulverizes",
+  "pulverising": "pulverizing",
+  "pummelled": "pummel",
+  "pummelling": "pummeled",
+  "pyjama": "pajama",
+  "pyjamas": "pajamas",
+  "pzazz": "pizzazz",
+  "quarrelled": "quarreled",
+  "quarrelling": "quarreling",
+  "radicalise": "radicalize",
+  "radicalised": "radicalized",
+  "radicalises": "radicalizes",
+  "radicalising": "radicalizing",
+  "rancour": "rancor",
+  "randomise": "randomize",
+  "randomised": "randomized",
+  "randomises": "randomizes",
+  "randomising": "randomizing",
+  "rationalisation": "rationalization",
+  "rationalisations": "rationalizations",
+  "rationalise": "rationalize",
+  "rationalised": "rationalized",
+  "rationalises": "rationalizes",
+  "rationalising": "rationalizing",
+  "ravelled": "raveled",
+  "ravelling": "raveling",
+  "realisable": "realizable",
+  "realisation": "realization",
+  "realisations": "realizations",
+  "realise": "realize",
+  "realised": "realized",
+  "realises": "realizes",
+  "realising": "realizing",
+  "recognisable": "recognizable",
+  "recognisably": "recognizably",
+  "recognisance": "recognizance",
+  "recognise": "recognize",
+  "recognised": "recognized",
+  "recognises": "recognizes",
+  "recognising": "recognizing",
+  "reconnoitre": "reconnoiter",
+  "reconnoitred": "reconnoitered",
+  "reconnoitres": "reconnoiters",
+  "reconnoitring": "reconnoitering",
+  "refuelled": "refueled",
+  "refuelling": "refueling",
+  "regularisation": "regularization",
+  "regularise": "regularize",
+  "regularised": "regularized",
+  "regularises": "regularizes",
+  "regularising": "regularizing",
+  "remodelled": "remodeled",
+  "remodelling": "remodeling",
+  "remould": "remold",
+  "remoulded": "remolded",
+  "remoulding": "remolding",
+  "remoulds": "remolds",
+  "reorganisation": "reorganization",
+  "reorganisations": "reorganizations",
+  "reorganise": "reorganize",
+  "reorganised": "reorganized",
+  "reorganises": "reorganizes",
+  "reorganising": "reorganizing",
+  "revelled": "reveled",
+  "reveller": "reveler",
+  "revellers": "revelers",
+  "revelling": "reveling",
+  "revitalise": "revitalize",
+  "revitalised": "revitalized",
+  "revitalises": "revitalizes",
+  "revitalising": "revitalizing",
+  "revolutionise": "revolutionize",
+  "revolutionised": "revolutionized",
+  "revolutionises": "revolutionizes",
+  "revolutionising": "revolutionizing",
+  "rhapsodise": "rhapsodize",
+  "rhapsodised": "rhapsodized",
+  "rhapsodises": "rhapsodizes",
+  "rhapsodising": "rhapsodizing",
+  "rigour": "rigor",
+  "rigours": "rigors",
+  "ritualised": "ritualized",
+  "rivalled": "rivaled",
+  "rivalling": "rivaling",
+  "romanticise": "romanticize",
+  "romanticised": "romanticized",
+  "romanticises": "romanticizes",
+  "romanticising": "romanticizing",
+  "rumour": "rumor",
+  "rumoured": "rumored",
+  "rumours": "rumors",
+  "sabre": "saber",
+  "sabres": "sabers",
+  "saltpetre": "saltpeter",
+  "sanitise": "sanitize",
+  "sanitised": "sanitized",
+  "sanitises": "sanitizes",
+  "sanitising": "sanitizing",
+  "satirise": "satirize",
+  "satirised": "satirized",
+  "satirises": "satirizes",
+  "satirising": "satirizing",
+  "saviour": "savior",
+  "saviours": "saviors",
+  "savour": "savor",
+  "savoured": "savored",
+  "savouries": "savories",
+  "savouring": "savoring",
+  "savours": "savors",
+  "savoury": "savory",
+  "scandalise": "scandalize",
+  "scandalised": "scandalized",
+  "scandalises": "scandalizes",
+  "scandalising": "scandalizing",
+  "sceptic": "skeptic",
+  "sceptical": "skeptical",
+  "sceptically": "skeptically",
+  "scepticism": "skepticism",
+  "sceptics": "skeptics",
+  "sceptre": "scepter",
+  "sceptres": "scepters",
+  "scrutinise": "scrutinize",
+  "scrutinised": "scrutinized",
+  "scrutinises": "scrutinizes",
+  "scrutinising": "scrutinizing",
+  "secularisation": "secularization",
+  "secularise": "secularize",
+  "secularised": "secularized",
+  "secularises": "secularizes",
+  "secularising": "secularizing",
+  "sensationalise": "sensationalize",
+  "sensationalised": "sensationalized",
+  "sensationalises": "sensationalizes",
+  "sensationalising": "sensationalizing",
+  "sensitise": "sensitize",
+  "sensitised": "sensitized",
+  "sensitises": "sensitizes",
+  "sensitising": "sensitizing",
+  "sentimentalise": "sentimentalize",
+  "sentimentalised": "sentimentalized",
+  "sentimentalises": "sentimentalizes",
+  "sentimentalising": "sentimentalizing",
+  "sepulchre": "sepulcher",
+  "sepulchres": "sepulchers",
+  "serialisation": "serialization",
+  "serialisations": "serializations",
+  "serialise": "serialize",
+  "serialised": "serialized",
+  "serialises": "serializes",
+  "serialising": "serializing",
+  "sermonise": "sermonize",
+  "sermonised": "sermonized",
+  "sermonises": "sermonizes",
+  "sermonising": "sermonizing",
+  "sheikh": "sheik",
+  "shovelled": "shoveled",
+  "shovelling": "shoveling",
+  "shrivelled": "shriveled",
+  "shrivelling": "shriveling",
+  "signalise": "signalize",
+  "signalised": "signalized",
+  "signalises": "signalizes",
+  "signalising": "signalizing",
+  "signalled": "signaled",
+  "signalling": "signaling",
+  "smoulder": "smolder",
+  "smouldered": "smoldered",
+  "smouldering": "smoldering",
+  "smoulders": "smolders",
+  "snivelled": "sniveled",
+  "snivelling": "sniveling",
+  "snorkelled": "snorkeled",
+  "snorkelling": "snorkeling",
+  "snowplough": "snowplow",
+  "snowploughs": "snowplow",
+  "socialisation": "socialization",
+  "socialise": "socialize",
+  "socialised": "socialized",
+  "socialises": "socializes",
+  "socialising": "socializing",
+  "sodomise": "sodomize",
+  "sodomised": "sodomized",
+  "sodomises": "sodomizes",
+  "sodomising": "sodomizing",
+  "solemnise": "solemnize",
+  "solemnised": "solemnized",
+  "solemnises": "solemnizes",
+  "solemnising": "solemnizing",
+  "sombre": "somber",
+  "specialisation": "specialization",
+  "specialisations": "specializations",
+  "specialise": "specialize",
+  "specialised": "specialized",
+  "specialises": "specializes",
+  "specialising": "specializing",
+  "spectre": "specter",
+  "spectres": "specters",
+  "spiralled": "spiraled",
+  "spiralling": "spiraling",
+  "splendour": "splendor",
+  "splendours": "splendors",
+  "squirrelled": "squirreled",
+  "squirrelling": "squirreling",
+  "stabilisation": "stabilization",
+  "stabilise": "stabilize",
+  "stabilised": "stabilized",
+  "stabiliser": "stabilizer",
+  "stabilisers": "stabilizers",
+  "stabilises": "stabilizes",
+  "stabilising": "stabilizing",
+  "standardisation": "standardization",
+  "standardise": "standardize",
+  "standardised": "standardized",
+  "standardises": "standardizes",
+  "standardising": "standardizing",
+  "stencilled": "stenciled",
+  "stencilling": "stenciling",
+  "sterilisation": "sterilization",
+  "sterilisations": "sterilizations",
+  "sterilise": "sterilize",
+  "sterilised": "sterilized",
+  "steriliser": "sterilizer",
+  "sterilisers": "sterilizers",
+  "sterilises": "sterilizes",
+  "sterilising": "sterilizing",
+  "stigmatisation": "stigmatization",
+  "stigmatise": "stigmatize",
+  "stigmatised": "stigmatized",
+  "stigmatises": "stigmatizes",
+  "stigmatising": "stigmatizing",
+  "storey": "story",
+  "storeys": "stories",
+  "subsidisation": "subsidization",
+  "subsidise": "subsidize",
+  "subsidised": "subsidized",
+  "subsidiser": "subsidizer",
+  "subsidisers": "subsidizers",
+  "subsidises": "subsidizes",
+  "subsidising": "subsidizing",
+  "succour": "succor",
+  "succoured": "succored",
+  "succouring": "succoring",
+  "succours": "succors",
+  "sulphate": "sulfate",
+  "sulphates": "sulfates",
+  "sulphide": "sulfide",
+  "sulphides": "sulfides",
+  "sulphur": "sulfur",
+  "sulphurous": "sulfurous",
+  "summarise": "summarize",
+  "summarised": "summarized",
+  "summarises": "summarizes",
+  "summarising": "summarizing",
+  "swivelled": "swiveled",
+  "swivelling": "swiveling",
+  "symbolise": "symbolize",
+  "symbolised": "symbolized",
+  "symbolises": "symbolizes",
+  "symbolising": "symbolizing",
+  "sympathise": "sympathize",
+  "sympathised": "sympathized",
+  "sympathiser": "sympathizer",
+  "sympathisers": "sympathizers",
+  "sympathises": "sympathizes",
+  "sympathising": "sympathizing",
+  "synchronisation": "synchronization",
+  "synchronise": "synchronize",
+  "synchronised": "synchronized",
+  "synchronises": "synchronizes",
+  "synchronising": "synchronizing",
+  "synthesise": "synthesize",
+  "synthesised": "synthesized",
+  "synthesiser": "synthesizer",
+  "synthesisers": "synthesizers",
+  "synthesises": "synthesizes",
+  "synthesising": "synthesizing",
+  "syphon": "siphon",
+  "syphoned": "siphoned",
+  "syphoning": "siphoning",
+  "syphons": "siphons",
+  "systematisation": "systematization",
+  "systematise": "systematize",
+  "systematised": "systematized",
+  "systematises": "systematizes",
+  "systematising": "systematizing",
+  "tantalise": "tantalize",
+  "tantalised": "tantalized",
+  "tantalises": "tantalizes",
+  "tantalising": "tantalizing",
+  "tantalisingly": "tantalizingly",
+  "tasselled": "tasseled",
+  "technicolour": "technicolor",
+  "temporise": "temporize",
+  "temporised": "temporized",
+  "temporises": "temporizes",
+  "temporising": "temporizing",
+  "tenderise": "tenderize",
+  "tenderised": "tenderized",
+  "tenderises": "tenderizes",
+  "tenderising": "tenderizing",
+  "terrorise": "terrorize",
+  "terrorised": "terrorized",
+  "terrorises": "terrorizes",
+  "terrorising": "terrorizing",
+  "theatre": "theater",
+  "theatregoer": "theatergoer",
+  "theatregoers": "theatergoers",
+  "theatres": "theaters",
+  "theorise": "theorize",
+  "theorised": "theorized",
+  "theorises": "theorizes",
+  "theorising": "theorizing",
+  "tonne": "ton",
+  "tonnes": "tons",
+  "towelled": "toweled",
+  "towelling": "toweling",
+  "toxaemia": "toxemia",
+  "tranquillise": "tranquilize",
+  "tranquillised": "tranquilized",
+  "tranquilliser": "tranquilizer",
+  "tranquillisers": "tranquilizers",
+  "tranquillises": "tranquilizes",
+  "tranquillising": "tranquilizing",
+  "tranquillity": "tranquility",
+  "tranquillize": "tranquilize",
+  "tranquillized": "tranquilized",
+  "tranquillizer": "tranquilizer",
+  "tranquillizers": "tranquilizers",
+  "tranquillizes": "tranquilizes",
+  "tranquillizing": "tranquilizing",
+  "tranquilly": "tranquility",
+  "transistorised": "transistorized",
+  "traumatise": "traumatize",
+  "traumatised": "traumatized",
+  "traumatises": "traumatizes",
+  "traumatising": "traumatizing",
+  "travelled": "traveled",
+  "traveller": "traveler",
+  "travellers": "travelers",
+  "travelling": "traveling",
+  "travelog": "travelogue",
+  "travelogs": "travelogues",
+  "trialled": "trialed",
+  "trialling": "trialing",
+  "tricolour": "tricolor",
+  "tricolours": "tricolors",
+  "trivialise": "trivialize",
+  "trivialised": "trivialized",
+  "trivialises": "trivializes",
+  "trivialising": "trivializing",
+  "tumour": "tumor",
+  "tumours": "tumors",
+  "tunnelled": "tunneled",
+  "tunnelling": "tunneling",
+  "tyrannise": "tyrannize",
+  "tyrannised": "tyrannized",
+  "tyrannises": "tyrannizes",
+  "tyrannising": "tyrannizing",
+  "tyre": "tire",
+  "tyres": "tires",
+  "unauthorised": "unauthorized",
+  "uncivilised": "uncivilized",
+  "underutilised": "underutilized",
+  "unequalled": "unequaled",
+  "unfavourable": "unfavorable",
+  "unfavourably": "unfavorably",
+  "unionisation": "unionization",
+  "unionise": "unionize",
+  "unionised": "unionized",
+  "unionises": "unionizes",
+  "unionising": "unionizing",
+  "unorganised": "unorganized",
+  "unravelled": "unraveled",
+  "unravelling": "unraveling",
+  "unrecognisable": "unrecognizable",
+  "unrecognised": "unrecognized",
+  "unrivalled": "unrivaled",
+  "unsavoury": "unsavory",
+  "untrammelled": "untrammeled",
+  "urbanisation": "urbanization",
+  "urbanise": "urbanize",
+  "urbanised": "urbanized",
+  "urbanises": "urbanizes",
+  "urbanising": "urbanizing",
+  "utilisable": "utilizable",
+  "utilisation": "utilization",
+  "utilise": "utilize",
+  "utilised": "utilized",
+  "utilises": "utilizes",
+  "utilising": "utilizing",
+  "valour": "valor",
+  "vandalise": "vandalize",
+  "vandalised": "vandalized",
+  "vandalises": "vandalizes",
+  "vandalising": "vandalizing",
+  "vaporisation": "vaporization",
+  "vaporise": "vaporize",
+  "vaporised": "vaporized",
+  "vaporises": "vaporizes",
+  "vaporising": "vaporizing",
+  "vapour": "vapor",
+  "vapours": "vapors",
+  "verbalise": "verbalize",
+  "verbalised": "verbalized",
+  "verbalises": "verbalizes",
+  "verbalising": "verbalizing",
+  "victimisation": "victimization",
+  "victimise": "victimize",
+  "victimised": "victimized",
+  "victimises": "victimizes",
+  "victimising": "victimizing",
+  "videodisc": "videodisk",
+  "videodiscs": "videodisks",
+  "vigour": "vigor",
+  "visualisation": "visualization",
+  "visualisations": "visualizations",
+  "visualise": "visualize",
+  "visualised": "visualized",
+  "visualises": "visualizes",
+  "visualising": "visualizing",
+  "vocalisation": "vocalization",
+  "vocalisations": "vocalizations",
+  "vocalise": "vocalize",
+  "vocalised": "vocalized",
+  "vocalises": "vocalizes",
+  "vocalising": "vocalizing",
+  "vulcanised": "vulcanized",
+  "vulgarisation": "vulgarization",
+  "vulgarise": "vulgarize",
+  "vulgarised": "vulgarized",
+  "vulgarises": "vulgarizes",
+  "vulgarising": "vulgarizing",
+  "waggon": "wagon",
+  "waggons": "wagons",
+  "watercolour": "watercolor",
+  "watercolours": "watercolors",
+  "weaselled": "weaseled",
+  "weaselling": "weaseling",
+  "westernisation": "westernization",
+  "westernise": "westernize",
+  "westernised": "westernized",
+  "westernises": "westernizes",
+  "westernising": "westernizing",
+  "womanise": "womanize",
+  "womanised": "womanized",
+  "womaniser": "womanizer",
+  "womanisers": "womanizers",
+  "womanises": "womanizes",
+  "womanising": "womanizing",
+  "woollen": "woolen",
+  "woollens": "woolens",
+  "woollies": "woolies",
+  "woolly": "wooly",
+  "worshipped": "worshiped",
+  "worshipper": "worshiper",
+  "worshipping": "worshiping",
+  "yodelled": "yodeled",
+  "yodelling": "yodeling",
+  "yoghourt": "yogurt",
+  "yoghourts": "yogurts",
+  "yoghurt": "yogurt",
+  "yoghurts": "yogurts"
+}
diff --git a/open-source-ai-game-jam/README.md b/open-source-ai-game-jam/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..79b699d2746d1a14c0b36d39c546244f5f7ad5db
--- /dev/null
+++ b/open-source-ai-game-jam/README.md
@@ -0,0 +1,147 @@
+# Welcome to the first [Open Source AI Game Jam](https://itch.io/jam/open-source-ai-game-jam) 🎮
+
+![](https://img.itch.zone/aW1hZ2UyL2phbS8zMzExNDMvMTIyNDYzNzYucG5n/original/lXt9Rf.png)
+
+Welcome to the **first Open-Source AI Game Jam 🎉**. During two days, **you’ll make a game using AI tools 🤖.**
+
+🤝 Open to all skill levels
+
+💸 Participation fee: Free
+
+📍 Where?: Online
+
+**Claim your spot in the Game Jam! Sign up here** 👉 https://itch.io/jam/open-source-ai-game-jam
+
+This document summarizes all the relevant information required for the Game Jam 📋. **Please read it thoroughly and make sure to**:
+
+- Do the Onboarding ⛴️
+- Read the The Game Jam Rules 📜
+- Join the Discord Server 👉 https://hf.co/join/discord
+
+# The Onboarding ⛴️
+
+When the game jam starts here’s what you need to do:
+
+🔢 If it’s not already done, don’t forget to **sign up to the Game Jam to be able to summit your game** 👉 https://itch.io/jam/open-source-ai-game-jam
+
+2️⃣ Watch the video below that will give you the Game Jam Theme **(the video will be posted on Friday 7th of July at 5:00 UTC)**.
+
+**The Theme Announcement** 👉 https://youtu.be/k0MvSAwoM8k
+
+3️⃣ Sign up to the Discord Server 👉 https://hf.co/join/discord
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/events/open-source-game-jam/discord.png" alt="Discord"/>
+
+4️⃣ In *channels and role* select ML For Game Development
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/events/open-source-game-jam/discord-role.png" alt="Discord-role"/>
+
+5️⃣ You'll see we created 4 channels for the game Jam
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/events/open-source-game-jam/discord-channels.png" alt="Discord"/>
+
+6️⃣ You **search for a team or teammates**? Ask on **#GameJam-Looking-For-Team**
+
+7️⃣ You have questions? Ask on **Ask on #GameJam-Help,** we’ll be there to respond 🤗
+
+8️⃣ When you have your team or you want to work alone, it’s time to start to make your game. **Keep Discord open because we’ll give update from time to time** 🤗
+
+
+## The Goal of this Game Jam 🏆
+
+Create a game in **48 hours** that uses **at** **least one Open Source AI Tool**
+
+You can use proprietary AI tools (Midjourney, ChatGPT) **as long as at least one open source tool is part of the game or workflow**.
+
+## The Game Jam Rules 📜
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/events/open-source-game-jam/rules.png" alt="Rules"/>
+
+## Deadlines 🕧
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/events/open-source-game-jam/deadlines.png" alt="Deadlines"/>
+
+### Voting System 🗳️
+
+- After the submission deadline (July 9th at 5:00pm UTC) you’ll **get until July 16th to vote for the other games**
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/events/open-source-game-jam/voting.png" alt="Voting"/>
+
+## The AI Toolbox 🧰
+
+The AI toolbox 🧰 (you can use other AI tools too): https://github.com/simoninithomas/awesome-ai-tools-for-game-dev
+
+Here some examples of AI tools you can use (again remember that you need to use at least one Open Source AI model):
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/events/open-source-game-jam/toolbox1.png" alt="Toolbox1"/>
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/events/open-source-game-jam/toolbox2.png" alt="Toolbox2"/>
+
+
+## Some helpful tutorials 📖
+
+Here's some helpful tutorials:
+- How to install the Unity Hugging Face API: https://huggingface.co/blog/unity-api
+- AI Speech Recognition in Unity: https://huggingface.co/blog/unity-asr
+- Making ML-powered web games with Transformers.js: https://huggingface.co/blog/ml-web-games
+- Building a smart Robot AI using Hugging Face 🤗 and Unity: https://thomassimonini.substack.com/p/building-a-smart-robot-ai-using-hugging
+
+## Some Game Examples 🕹️
+
+Here we give some Game Examples which use AI tools:
+
+1. **Detective Game**
+    
+    You can play it here 👉 https://google.github.io/mysteryofthreebots/
+    
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/events/open-source-game-jam/example1.png" alt="Example1"/>
+    
+2. **Action Game**
+    
+    You can play it here 👉 https://huggingface.co/spaces/ThomasSimonini/SmartRobot
+    Tutorial 👉 Building a smart Robot AI using Hugging Face 🤗 and Unity
+    
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/events/open-source-game-jam/example2.png" alt="Example2"/>
+    
+3. **AI NPC with Unity MLAgents**
+
+You can play it here 👉 https://danielk0703.itch.io/ship-jam
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/events/open-source-game-jam/example3.png" alt="Example3"/>
+
+1. **Example 4: Doodle Dash**
+
+Play it here 👉 https://huggingface.co/spaces/Xenova/doodle-dash
+
+Learn to make your own with this tutorial 👉 https://huggingface.co/blog/ml-web-games
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/events/open-source-game-jam/example4.png" alt="Example4"/>
+
+## Some advice 💡
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/events/open-source-game-jam/advice.png" alt="Advice"/>
+
+
+## Discord Channels
+
+Our Discord Server is the **central place to create teams, exchange with other teams, ask questions and get the latest updates**.
+
+👉 https://hf.co/join/discord
+
+We built different channels:
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/events/open-source-game-jam/discord-channels.png" alt="Channels"/>
+
+## You're looking for a team?
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/events/open-source-game-jam/looking-for-team.png" alt="Channels"/>
+
+## You have some questions? 
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/events/open-source-game-jam/questions.png" alt="Channels"/>
+
+## Organizers 🧑‍🤝‍🧑
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/events/open-source-game-jam/organizers.png" alt="Organizers"/>
+
+
+
+
diff --git a/preprocessor_config.json b/preprocessor_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..91876762a536a746d268353c5cba57286e76b058
--- /dev/null
+++ b/preprocessor_config.json
@@ -0,0 +1,14 @@
+{
+  "chunk_length": 30,
+  "feature_extractor_type": "WhisperFeatureExtractor",
+  "feature_size": 80,
+  "hop_length": 160,
+  "n_fft": 400,
+  "n_samples": 480000,
+  "nb_max_frames": 3000,
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "processor_class": "WhisperProcessor",
+  "return_attention_mask": false,
+  "sampling_rate": 16000
+}
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f21b077d7131224bb89056aaa08283ec6931090e
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,10 @@
+accelerate
+torch
+torchvision
+datasets
+scipy
+wandb
+einops
+fire
+retry
+kornia==0.5.4
\ No newline at end of file
diff --git a/setup.py b/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..e73ef0b5ff20407861f4e667fb42a1753b68027a
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,6 @@
+from setuptools import setup, find_packages
+
+with open("requirements.txt", "r") as f:
+    requirements = f.read().splitlines()
+
+setup(name="huggan", install_requires=requirements, packages=find_packages())
diff --git a/sklearn-sprint/guidelines.md b/sklearn-sprint/guidelines.md
new file mode 100644
index 0000000000000000000000000000000000000000..d092fa7249ecb584a738b7956fa9210f85a385f2
--- /dev/null
+++ b/sklearn-sprint/guidelines.md
@@ -0,0 +1,101 @@
+
+![Hugging Face x Scikit-learn](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hfxsklearn.png)
+
+In this sprint, we will build interactive demos from the scikit-learn documentation and, afterwards, contribute the demos directly to the docs.
+
+## Important Dates
+
+🌅 Sprint Start Date: Apr 12, 2023
+🌃 Sprint Finish Date: Apr 30, 2023
+
+## To get started 🤩
+
+1. Join our [Discord](https://huggingface.co/join/discord) and take the role #sklearn-sprint-participant by selecting "Sklearn Working Group" in the #role-assignment channel. Then, meet us in #sklearn-sprint channel.
+2. Head to [this page](https://scikit-learn.org/stable/auto_examples/) and pick an example you’d like to build on. 
+3. Leave a comment on [this spreadsheet](https://docs.google.com/spreadsheets/d/14EThtIyF4KfpU99Fm2EW3Rz9t6SSEqDyzV4jmw3fjyI/edit?usp=sharing) with your name under Owner column, claiming the example. The spreadsheet has a limited number of examples. Feel free to add yours with a comment if it doesn’t exist in the spreadsheet.
+.
+4. Start building!
+    
+    We will be hosting our applications in [scikit-learn](https://huggingface.co/sklearn-docs) organization of Hugging Face. 
+    
+    For complete starters: in the Hugging Face Hub, there are repositories for models, datasets, and [Spaces](https://huggingface.co/spaces). Spaces are a special type of repository hosting ML applications, such as showcasing a model. To write our apps, we will only be using Gradio. [Gradio](https://gradio.app/) is a library that lets you build a cool front-end application for your models, completely in Python, and supports many libraries! In this sprint, we will be using mostly visualization support (`matplotlib`, `plotly`, `altair` and more) and [skops](https://skops.readthedocs.io/en/stable/) integration (which you can launch an interface for a given classification or regression interface with one line of code). 
+    
+    In Gradio, there are two ways to create a demo. One is to use `Interface`, which is a very simple abstraction. Let’s see an example.
+    
+    ```python
+    import gradio as gr
+    
+    # implement your classifier here 
+    clf.fit(X_train, y_train)
+
+    def cancer_classifier(df):
+        # simply infer and return predictions
+        predictions = clf.predict(df)
+        return predictions
+    
+    gr.Interface(fn=cancer_classifier, inputs="dataframe", 
+    outputs="label").launch()
+    
+    # save this in a file called app.py
+    # then run it 
+    ```
+    
+    This will result in following interface:
+    
+    ![Simple Interface](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/interface.png)
+    
+    This is very customizable. You can specify rows and columns, add a title and description, an example input, and more. There’s a more detailed guide [here](https://gradio.app/using-gradio-for-tabular-workflows/). 
+    
+    Another way of creating an application is to use [Blocks](https://gradio.app/quickstart/#blocks-more-flexibility-and-control). You can see usage of Blocks in the example applications linked in this guide. 
+    
+    After we create our application, we will create a Space. You can go to [hf.co](http://huggingface.co), click on your profile on top right and select “New Space”.
+    
+    ![New Space](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/new_space.png)
+    
+    We can name our Space, pick a license and select Space SDK as “Gradio”. Free hardware is enough for our app, so no need to change it.
+    
+    ![Space Configuration](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/space_config.png)
+    
+    After creating the Space, you have three options
+     * You can clone the repository locally, add your files, and then push them to the Hub.
+     * You can do all your coding directly in the browser.
+     *  (shown below) You can do the coding locally and then drag and drop your application file to the Hub.
+    
+    ![Space Config](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/space_config.png)
+    
+    To upload your application file, pick “Add File” and drag and drop your file.
+    
+    ![New Space Landing](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/space_landing.png)
+    
+    Lastly, if your application includes any library other than Gradio, create a file called requirements.txt and add requirements like below: 
+    
+    ```python
+    matplotlib==3.6.3
+    scikit-learn==1.2.1
+    ```
+    
+     And your app should be up and running!
+    
+    **Example Submissions**
+    
+    We left couple of examples below: (there’s more at the end of this page)
+    Documentation page for comparing linkage methods for hierarchical clustering and example Space built on it 👇🏼 
+    
+    [Comparing different hierarchical linkage methods on toy datasets](https://scikit-learn.org/stable/auto_examples/cluster/plot_linkage_comparison.html#sphx-glr-auto-examples-cluster-plot-linkage-comparison-py)
+    
+    [Hierarchical Clustering Linkage - a Hugging Face Space by scikit-learn](https://huggingface.co/spaces/scikit-learn/hierarchical-clustering-linkage)
+    
+    Note: If for your demo you're training a model from scratch (e.g. training an image classifier), you can push it to the Hub using [skops](https://skops.readthedocs.io/en/stable/) and build a Gradio demo on top of it.  For such submission, we expect a model repository with a model card and the model weight as well as a simple Space with the interface that receives input and outputs results. You can use this tutorial to get started with [skops](https://www.kdnuggets.com/2023/02/skops-new-library-improve-scikitlearn-production.html).
+    
+    You can find an example submission for a model repository below.
+    
+    [scikit-learn/cancer-prediction-trees · Hugging Face](https://huggingface.co/scikit-learn/cancer-prediction-trees)
+    
+4. After the demos are done, we will open pull requests to scikit-learn documentation in [scikit-learn’s repository](https://github.com/scikit-learn/scikit-learn) to contribute our application codes to be directly inside the documentation. We will help you out if this is your first open source contribution. 🤗 
+
+**If you need any help** you can join our discord server, take collaborate role and join `sklearn-sprint` channel and ask questions 🤗🫂 
+
+### Sprint Prizes
+We will be giving following vouchers that can be spent at [Hugging Face Store](https://store.huggingface.co/) including shipping,
+- $20 worth of voucher for everyone that builds three demos,
+- $40 worth of voucher for everyone that builds five demos. 
\ No newline at end of file
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new file mode 100644
index 0000000000000000000000000000000000000000..bf69932dca4b3719b59fdd8f6cc1978109509f6c
--- /dev/null
+++ b/special_tokens_map.json
@@ -0,0 +1,139 @@
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new file mode 100644
index 0000000000000000000000000000000000000000..d13b786c04765fb1a06492b53587752cd67665ea
--- /dev/null
+++ b/tokenizer_config.json
@@ -0,0 +1,12989 @@
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diff --git a/whisper-fine-tuning-event/README.md b/whisper-fine-tuning-event/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..b62f501c0b35cc51b7ce86a9cf514b357cfe0873
--- /dev/null
+++ b/whisper-fine-tuning-event/README.md
@@ -0,0 +1,1244 @@
+# Whisper Fine-Tuning Event 🤗
+
+Welcome to the Whisper fine-tuning event 🎙️!
+
+For two weeks, we will endeavour to fine-tune the Whisper model to build state-of-the-art speech recognition systems in 
+the languages of our choice 🗣. We will work together as a community to achieve this, helping others and learning where 
+we can 🤗. If necessary and available, free access to A100 40 GB GPUs will kindly be provided by our cloud compute 
+partners, [Lambda](https://lambdalabs.com) 🚀.
+
+This document summarises all the relevant information required for the event 📋. Please read it thoroughly 
+and make sure to:
+- Sign-up using the [Google form](https://forms.gle/F2bpouvhDpKKisM39)
+- Join the [Hugging Face Discord server](https://hf.co/join/discord) and make sure to assign yourself **@ml-4-audio** role in #role-assignment so that you can access #events channel. 
+
+## Table of Contents
+
+- [Introduction](#introduction)
+- [Important Dates](#important-dates)
+- [Launch a Lambda Cloud GPU](#launch-a-lambda-cloud-gpu)
+- [Set Up an Environment](#set-up-an-environment)
+- [Data and Pre-Processing](#data-and-pre-processing)
+- [Fine-Tune a Whisper Model](#fine-tune-whisper)
+- [Evaluation](#evaluation)
+- [Building a Demo](#building-a-demo)
+- [Communication and Problems](#communication-and-problems)
+- [Talks](#talks)
+- [Tips and Tricks](#tips-and-tricks)
+- [Feedback](#feedback)
+
+## Introduction
+Whisper is a pre-trained model for automatic speech recognition (ASR) published in [September 2022](https://openai.com/blog/whisper/) 
+by the authors Radford et al. from OpenAI. Pre-trained on 680,000 hours of labelled data, it demonstrates a strong ability 
+to generalise to different datasets and domains. Through fine-tuning, the performance of this model can be significantly 
+boosted for a given language.
+
+In this event, we're bringing the community together to fine-tune Whisper in as many languages as possible. Our aim is 
+to achieve state-of-the-art on the languages spoken by the community. Together, we can democratise speech recognition 
+for all.
+
+We are providing training scripts, notebooks, blog posts, talks and compute (where available), so you have all the 
+resources you need to participate! You are free to chose your level of participation, from using the template script and setting 
+it to your language, right the way through to exploring advanced training methods. We encourage you to participate to 
+level that suits you best. We'll be on hand to facilitate this!
+
+Participants are allowed to fine-tune their systems on the training data of their choice, including datasets from the 
+Hugging Face Hub, web-scraped data from the internet, or private datasets. Whisper models will be evaluated 
+on the "test" split of the [Common Voice 11](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) 
+dataset for the participant's chosen language.
+
+We believe that framing the event as a competition is fun! But at the core, the event is about
+fine-tuning Whisper in as many languages as possible as a community. We want to foster an environment where we 
+work together, help each other solve bugs, share important findings and ultimately learn something new.
+
+This README contains all the information you need for the event. It is structured such that you can read it sequentially, 
+section-by-section. **We recommend that you read the document once from start to finish before running any code.** This will 
+give you an idea of where to look for the relevant information and an idea of how the event is going to run.
+
+## Important Dates
+
+- *Introduction Talk*: 2nd December 2022
+- *Sprint start*: 5th December 2022
+- *Speaker Events*: 5th December 2022
+- *Sprint end*: 19th December 2022
+- *Results*: 23rd December 2022
+
+## Launch a Lambda Cloud GPU
+Where possible, we encourage you to fine-tune Whisper on a local GPU machine. This will mean a faster set-up and more 
+familiarity with your device. If you are running on a local GPU machine, you can skip ahead to the next section: [Set Up an Environment](#set-up-an-environment). 
+
+The training scripts can also be run as a notebook through Google Colab. We recommend you train on Google Colab if you 
+have a "Colab Pro" or "Pro+" subscription. This is to ensure that you receive a sufficiently powerful GPU on your Colab for 
+fine-tuning Whisper. If you wish to fine-tune Whisper through Google Colab, you can skip ahead to the section: [Data and Pre-Processing](#data-and-pre-processing). 
+
+If you do not have access to a local GPU or Colab Pro/Pro+, we'll endeavour to provide you with a cloud GPU instance.
+We've partnered up with Lambda to provide cloud compute for this event. They'll be providing the latest NVIDIA A100 
+40 GB GPUs, so you'll be loaded with some serious firepower! The Lambda API makes it easy to spin-up and launch 
+a GPU instance. In this section, we'll go through the steps for spinning up an instance one-by-one.
+
+<p align="center" width="100%">
+    <img width="50%" src="https://raw.githubusercontent.com/sanchit-gandhi/codesnippets/main/hf_x_lambda.jpg">
+</p>
+
+This section is split into three parts:
+
+1. [Signing-Up with Lambda](#signing-up-with-lambda)
+2. [Creating a Cloud Instance](#creating-a-cloud-instance)
+3. [Deleting a Cloud Instance](#deleting-a-cloud-instance)
+
+### Signing-Up with Lambda
+
+1. Create an account with Lambda using your email address of choice: https://cloud.lambdalabs.com/sign-up. If you already have an account, skip to step 2.
+2. Using this same email address, email `cloud@lambdal.com` with the Subject line: `Lambda cloud account for HuggingFace Whisper event - payment authentication and credit request`.
+3. Each user who emails as above will receive $110 in credits (amounting to 100 hours of 1x A100 usage).
+4. Register a valid payment method with Lambda in order to redeem the credits (see instructions below).
+
+100 hours of 1x A100 usage should enable you to complete 5-10 fine-tuning runs. To redeem these credits, you will need to 
+authorise a valid payment method with Lambda. Provided that you remain within $110 of compute spending, your card **will not** 
+be charged 💸. Registering your card with Lambda is a mandatory sign-up step that we unfortunately cannot bypass. But we 
+reiterate: you will not be charged provided you remain within $110 of compute spending!
+
+Follow steps 1-4 in the next section [Creating a Cloud Instance](#creating-a-cloud-instance) to register your
+card. If you experience issues with registering your card, contact the Lambda team on Discord (see [Communications and Problems](#communication-and-problems)).
+
+In order to maximise the free GPU hours you have available for training, we advise that you shut down GPUs when you are 
+not using them and closely monitor your GPU usage. We've detailed the steps you can follow to achieve this in [Deleting a Cloud Instance](#deleting-a-cloud-instance).
+
+### Creating a Cloud Instance
+Estimated time to complete: 5 mins
+
+*You can also follow our video tutorial to set up a cloud instance on Lambda* 👉️ [YouTube Video](https://www.youtube.com/watch?v=Ndm9CROuk5g&list=PLo2EIpI_JMQtncHQHdHq2cinRVk_VZdGW)
+
+1. Click the link: https://cloud.lambdalabs.com/instances
+2. You'll be asked to sign in to your Lambda account (if you haven't done so already).
+3. Once on the GPU instance page, click the purple button "Launch instance" in the top right.
+4. Verify a payment method if you haven't done so already. IMPORTANT: if you have followed the instructions in the previous section, you will have received $110 in GPU credits. Exceeding 100 hours of 1x A100 usage may incur charges on your credit card. Contact the Lambda team on Discord if you have issues authenticating your payment method (see [Communications and Problems](#communication-and-problems))
+5. Launching an instance:
+   1. In "Instance type", select the instance type "1x A100 (40 GB SXM4)"
+   2. In "Select region", select the region with availability closest to you.
+   3. In "Select filesystem", select "Don't attach a filesystem".
+6. You will be asked to provide your public SSH key. This will allow you to SSH into the GPU device from your local machine.
+   1. If you’ve not already created an SSH key pair, you can do so with the following command from your local device: 
+      ```bash
+      ssh-keygen
+      ```
+   2. You can find your public SSH key using the command: 
+      ```bash
+      cat ~/.ssh/id_rsa.pub
+      ```
+      (Windows: `type C:UsersUSERNAME.sshid_rsa.pub` where `USERNAME` is the name of your user)
+   4. Copy and paste the output of this command into the first text box
+   5. Give your SSH key a memorable name (e.g. `sanchits-mbp`)
+   6. Click "Add SSH Key"
+7. Select the SSH key from the drop-down menu and click "Launch instance"
+8. Read the terms of use and agree
+9. We can now see on the "GPU instances" page that our device is booting up!
+10. Once the device status changes to "✅ Running", click on the SSH login ("ssh ubuntu@..."). This will copy the SSH login to your clipboard.
+11. Now open a new command line window, paste the SSH login, and hit Enter.
+12. If asked "Are you sure you want to continue connecting?", type "yes" and press Enter.
+13. Great! You're now SSH'd into your A100 device! We're now ready to set up our Python environment!
+
+You can see your total GPU usage from the Lambda cloud interface: https://cloud.lambdalabs.com/usage
+
+Here, you can see the total charges that you have incurred since the start of the event. We advise that you check your 
+total on a daily basis to make sure that it remains below the credit allocation of $110. This ensures that you are 
+not inadvertently charged for GPU hours.
+
+If you are unable to SSH into your Lambda GPU in step 11, there is a workaround that you can try. On the [GPU instances page](https://cloud.lambdalabs.com/instances), 
+under the column "Cloud IDE", click the button "Launch". This will launch a Jupyter Lab on your GPU which will be displayed in your browser. In the 
+top left-hand corner, click "File" -> "New" -> "Terminal". This will open up a new terminal window. You can use this 
+terminal window to set up your Python environment in the next section [Set Up an Environment](#set-up-an-environment).
+
+### Deleting a Cloud Instance
+
+100 1x A100 hours should provide you with enough time for 5-10 fine-tuning runs (depending on how long you train for 
+and which size models). To maximise the GPU time you have for training, we advise that you shut down GPUs over prolonged 
+periods of time when they are not in use. Leaving a GPU running accidentally over the weekend will incur 48 hours of 
+wasted GPU hours. That's nearly half of your compute allocation! So be smart and shut down your GPU when you're not training.
+
+Creating an instance and setting it up for the first time may take up to 20 minutes. Subsequently, this process will 
+be much faster as you gain familiarity with the steps, so you shouldn't worry about having to delete a GPU and spinning one 
+up the next time you need one. You can expect to spin-up and delete 2-3 GPUs over the course of the fine-tuning event.
+
+
+We'll quickly run through the steps for deleting a Lambda GPU. You can come back to these steps after you've 
+performed your first training run and you want to shut down the GPU:
+
+1. Go to the instances page: https://cloud.lambdalabs.com/instances
+2. Click the checkbox on the left next to the GPU device you want to delete
+3. Click the button "Terminate" in the top right-hand side of your screen (under the purple button "Launch instance")
+4. Type "erase data on instance" in the text box and press "ok"
+
+Your GPU device is now deleted and will stop consuming GPU credits.
+
+## Set Up an Environment
+Estimated time to complete: 5 mins
+
+*Follow along our video tutorial detailing the set up* 👉️ [YouTube Video](https://www.youtube.com/playlist?list=PLo2EIpI_JMQtzC5feNpqQL7eToYKcOxYf)
+
+The Whisper model should be fine-tuned using **PyTorch**, **🤗 Transformers**, and, **🤗 Datasets**. In this 
+section, we'll cover how to set up an environment with the required libraries. This section assumes that you are SSH'd 
+into your GPU device. This section does not apply if you are fine-tuning the Whisper model in a Google Colab.
+
+If you are returning to this section having read through it previously and want to quickly set up an environment, you 
+can do so in one call by executing the following code cell. If this is your first time setting up an environment, we 
+recommend you read this section to understand the steps involved.
+
+```bash
+sudo add-apt-repository -y ppa:jonathonf/ffmpeg-4
+sudo apt update
+sudo apt install -y ffmpeg
+
+sudo apt-get install git-lfs
+
+python3 -m venv hf_env
+source hf_env/bin/activate
+echo "source ~/hf_env/bin/activate" >> ~/.bashrc
+
+git clone https://github.com/huggingface/community-events.git
+pip install -r community-events/whisper-fine-tuning-event/requirements.txt
+
+git config --global credential.helper store
+huggingface-cli login
+ ```
+
+### Unix Libraries
+
+First, we need to make sure we have the required NVIDIA drivers installed. We can check that we have these drivers 
+through the following command:
+
+```bash
+nvidia-smi
+```
+
+This should print a table with our NVIDIA driver version and CUDA version, and should work out of the box for Lambda GPUs!
+If you get an error running this command, refer to your device manual for installing the required NVIDIA driver.
+
+Before installing the required libraries, we'd need to install and update the Unix package `ffmpeg` to version 4:
+
+ ```bash
+sudo add-apt-repository -y ppa:jonathonf/ffmpeg-4
+sudo apt update
+sudo apt install -y ffmpeg
+ ```
+
+We'll also need the package `git-lfs` to push large model weights to the Hugging Face Hub. To check whether 
+`git-lfs` is installed, simply run:
+
+```bash
+git-lfs -v
+```
+
+The output should show something like `git-lfs/2.13.2 (GitHub; linux amd64; go 1.15.4)`. If your console states that 
+the `git-lfs` command was not found, you can install it via: 
+
+```bash
+sudo apt-get install git-lfs
+```
+
+### Python Libraries
+
+We recommend installing the required libraries in a Python virtual environment. If you're unfamiliar with Python virtual 
+environments, check out the [official user guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).
+
+Let's define a variable that denotes the name of the environment we're going to create:
+
+```bash
+env_name=<your-venv-name>
+```
+
+We can create a virtual environment (venv) with this name using the following command:
+
+```bash
+python3 -m venv $env_name
+```
+
+We'll instruct our bash shell to activate the venv by default by placing the venv source command in `.bashrc`:
+
+```bash
+echo "source ~/$env_name/bin/activate" >> ~/.bashrc
+```
+
+Re-launching the bash shell will activate the venv:
+
+```bash
+bash
+```
+
+Great! We can see that our venv name is at the start of our command line - this means that we're operating from 
+within the venv. We can now go ahead and start installing the required Python packages to our venv.
+
+The [`requirements.txt`](https://github.com/huggingface/community-events/blob/main/whisper-fine-tuning-event/requirements.txt) 
+file in this directory has all the necessary Python packages we need to fine-tune Whisper, including PyTorch, Transformers 
+and Datasets. We'll install all the packages in this file through one `pip install` command.
+
+First, let's clone the `community-events` repository to our device:
+
+```bash
+git clone https://github.com/huggingface/community-events.git
+```
+
+Now we can install the packages from the `requirements.txt` file using the following command:
+
+```bash
+pip install -r community-events/whisper-fine-tuning-event/requirements.txt
+```
+
+Note: when installing packages, you might see warnings such as:
+
+```bash
+  error: invalid command 'bdist_wheel'
+  ----------------------------------------
+  ERROR: Failed building wheel for audioread
+```
+
+This is perfectly ok! It does not affect our installation.
+
+We can check that above steps installed the correct version of PyTorch to match our CUDA version. The following command should return True:
+
+```python
+python -c "import torch; print(torch.cuda.is_available())"
+```
+
+If the above command does not return True, refer to the [official instructions](https://pytorch.org/get-started/locally/) for installing PyTorch.
+
+We can now verify that `transformers` and `datasets` have been correctly installed. First, launch a Python shell:
+
+```bash
+python
+```
+
+Running the following code cell will load one sample of the [Common Voice](https://huggingface.co/datasets/common_voice) 
+dataset from the Hugging Face Hub and perform a forward pass of the "tiny" Whisper model:
+
+```python
+import torch
+from transformers import WhisperFeatureExtractor, WhisperForConditionalGeneration
+from datasets import load_dataset
+
+model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny")
+feature_extractor = WhisperFeatureExtractor.from_pretrained("openai/whisper-tiny")
+
+common_voice = load_dataset("common_voice", "en", split="validation", streaming=True)
+
+inputs = feature_extractor(next(iter(common_voice))["audio"]["array"], sampling_rate=16000, return_tensors="pt")
+input_features = inputs.input_features
+
+decoder_input_ids = torch.tensor([[1, 1]]) * model.config.decoder_start_token_id
+logits = model(input_features, decoder_input_ids=decoder_input_ids).logits
+
+print("Environment set up successful?", logits.shape[-1] == 51865)
+
+```
+
+If the final check returns True, the libraries have been installed correctly. Finally, exit the Python shell:
+
+```python
+quit()
+```
+
+The last thing we need to do is link our Hugging Face account. Run the command:
+
+```bash
+git config --global credential.helper store
+huggingface-cli login
+```
+
+And then enter an authentication token from https://huggingface.co/settings/tokens. Create a new token if you do not have 
+one already. You should make sure that this token has "write" privileges.
+
+## Data and Pre-Processing
+
+In this section, we will cover how to find suitable training data and the necessary steps to pre-process it. 
+If you are new to the 🤗 Datasets library, we highly recommend reading the comprehensive blog post: [A Complete Guide To Audio Datasets](https://huggingface.co/blog/audio-datasets). 
+This blog post will tell you everything you need to know about 🤗 Datasets and its one-line API.
+
+### Data
+
+Whisper models will be evaluated on the `"test"` split of the [Common Voice 11](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) 
+dataset. Any data can be used to fine-tune the Whisper model **except Common Voice's `"test"` split**. This exception 
+extends to all Common Voice versions, as the test split of legacy Common Voice releases often overlaps with the 
+latest one. For instance, the test split of Common Voice 10 is largely the same as that of Common Voice 11.
+
+So, the test data:
+
+```python
+load_dataset("mozilla-foundation/common_voice_11_0", "en", split="test", use_auth_token=True)
+```
+
+More or less includes the same data as:
+
+```python
+load_dataset("mozilla-foundation/common_voice_10_0", "en", split="test", use_auth_token=True)
+```
+
+And **neither** are allowed for training purposes. However, we strongly encourage participants to make use of the other 
+Common Voice splits for training data, such as the `"train"` and `"validation"` splits:
+
+```python
+load_dataset("mozilla-foundation/common_voice_10_0", "en", split="train", use_auth_token=True)
+```
+
+For most languages, the `"train"` split of Common Voice 11 dataset offers a reasonable amount of training data. 
+For low-resource languages, it is normal procedure to combine the `"train"` and `"validation"` splits to give a larger 
+training corpus:
+
+```python
+load_dataset("mozilla-foundation/common_voice_10_0", "en", split="train+validation", use_auth_token=True)
+```
+
+This notation for combining splits (`"split_a+split_b"`) is consistent for all resources in the event. You can combine 
+splits in this same way using the fine-tuning scripts in the following section [Fine-Tune Whisper](#fine-tune-whisper).
+
+If combining the `"train"` and `"validation"` splits of the Common Voice 11 dataset still gives insufficient training 
+data for your language, you can explore using other datasets on the Hub to train your model and try 
+[Mixing Datasets](#mixing-datasets-optional) to give larger training splits.
+
+### Streaming Mode
+
+Audio datasets are very large. This causes two issues:
+1. They require a significant amount of **storage space** to download.
+2. They take a significant amount of **time** to download and process.
+
+The storage and time requirements present limitations to most speech researchers. For example, downloading the English 
+subset of the Common Voice 11 dataset (2,300 hours) requires upwards of 200GB of disk space and up to several hours 
+of download time. For these reasons, we **do not** recommend that you run the following code cell! 
+```python
+from datasets import load_dataset
+
+common_voice = load_dataset("mozilla-foundation/common_voice_11_0", "en", use_auth_token=True)
+
+# we have to wait several hours until the entire dataset is downloaded before we can access the first sample... 
+print(next(iter(common_voice["train"])))
+```
+
+However, both these issues can be solved with 🤗 Datasets. Rather than downloading the whole dataset at once, we 
+load individual samples as we cycle over the dataset, in a process called _streaming_. Since the data is loaded 
+progressively as we iterate over the dataset, we can get started with a dataset as soon as the first sample is ready. 
+This way, we don't have to wait for the entire dataset to download before we can run our code! We are also free of any 
+disk space contraints: once we're done with a sample, we discard it and load the next one to memory. This way, we only 
+have the data when we need it, and not when we don't!
+
+Streaming is enabled by passing the argument `streaming=True` to the `load_dataset` function. We can then use our 
+audio datasets in much the same way as before! For these reasons, **we highly recommend** that you try out the following 
+code cell! Just make sure you've accepted the Common Voice 11 [terms of use](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) on the Hugging Face Hub.
+
+```python
+from datasets import load_dataset
+
+common_voice = load_dataset("mozilla-foundation/common_voice_11_0", "en", use_auth_token=True, streaming=True)
+
+# get the first sample of the dataset straight away!
+print(next(iter(common_voice["train"])))
+```
+
+The examples for this event rely heavily on streaming mode to fine-tune Whisper. With streaming mode, we can use **any 
+speech recognition dataset on the Hub with just 20GB of disk space**. As a speech recognition practitioner, this is 
+game changing! The largest speech recognition datasets are available to us regardless of our device disk space. We are 
+extremely excited to be showcasing streaming mode in this event and hope that you will enjoy using it.
+
+There is one caveat to streaming mode. When downloading a dataset to disk, the processed data is saved to our cache. If 
+we want to re-use this data, we can directly load the processed data from cache, skipping the download and processing 
+steps. Consequently, we only have to perform the downloading and processing operations once. With streaming mode, the 
+data is not downloaded to disk. Thus, neither the download nor pre-processing are cached. If we want to re-use the data, 
+the streaming steps must be repeated, with the audio files loaded and pre-processed again. Therefore, we recommend not 
+using streaming mode if your dataset is small (< 10 hours). In this case, it is faster to download and pre-process the 
+dataset in the conventional way once at the start, and then re-use it at each epoch. We provide pointers for disabling 
+streaming mode in the section [Fine-Tune Whisper](#fine-tune-whisper).
+
+If you want to read more about streaming mode, we 
+recommend you check out the aforementioned blog post: [A Complete Guide To Audio Datasets](https://huggingface.co/blog/audio-datasets).
+
+### Pre-Processing
+
+Data pre-processing is a very grey area when it comes to speech recognition. In this section, we'll try to make the 
+situation as clear as possible for you as participants.
+
+The Common Voice dataset is both cased and punctuated:
+
+```python
+print(next(iter(common_voice["train"]))["sentence"])
+```
+**Print Output:**
+```
+Why does Melissandre look like she wants to consume Jon Snow on the ride up the wall?
+```
+
+If we train the Whisper model on the raw Common Voice dataset, it will learn to predict casing and punctuation. This is 
+great when we want to use out model for actual speech recognition applications, such as transcribing meetings or 
+dictation, as the predicted transcriptions will be formatted with casing and punctuation.
+
+However, we also have the option of 'normalising' the dataset to remove any casing and punctuation. Normalising the 
+dataset makes the speech recognition task easier: the model no longer needs to distinguish between upper and lower case 
+characters, or have to predict punctuation from the audio data alone. Because of this, the word error rates are 
+naturally lower (meaning the results are better). The Whisper paper demonstrates the drastic effect that normalising 
+transcriptions can have on WER results (_c.f._ Section 4.4 of the [Whisper paper](https://cdn.openai.com/papers/whisper.pdf)). 
+But while we get lower WERs, we can't necessarily use our model in production. The lack of casing and punctuation makes 
+the predicted text from the model much harder to read. We would need additional post-processing models to restore casing and 
+punctuation in our predictions if we wanted to use it for downstream applications.
+
+There is a happy medium between the two: we can train our systems on cased and normalised transcriptions, and then 
+evaluate them on normalised text. This way, we train our systems to predict fully formatted text, but also benefit from 
+the WER improvements we get by normalising the transcriptions.
+
+The choice of whether you normalise the transcriptions is ultimately down to you. We recommend training on un-normalised 
+text and evaluating on normalised text to get the best of both worlds. Since those choices are not always obvious, feel 
+free to ask on Discord or (even better) post your question on the [forum](https://discuss.huggingface.co).
+
+| Train         | Eval          | Pros                                                           | Cons                                     |
+|---------------|---------------|----------------------------------------------------------------|------------------------------------------|
+| Un-normalised | Un-normalised | * Predict casing + punctuation<br>* One logic for train / eval | * WERs are higher                        |
+| Un-normalised | Normalised    | * Predict casing + punctuation<br>* WERs are lower             | * Different logic for train / eval       |
+| Normalised    | Normalised    | * One logic for train / eval<br>* WERs are lower               | * No casing / punctuation in predictions |
+
+With the provided training scripts, it is trivial to toggle between removing or retaining punctuation and casing, 
+requiring at most three lines of code change. Switching between the different modes is explained in more detail in the 
+following section [Fine-Tune Whisper](#fine-tune-whisper).
+
+If you want to find out more about pre- and post-processing for speech recognition, we refer you in the direction of 
+the paper: [ESB: A Benchmark For Multi-Domain End-to-End Speech Recognition](https://arxiv.org/abs/2210.13352).
+
+The following two subsections are optional. They cover how you can mix datasets to form larger training splits and how 
+you can use custom data to fine-tune your model. If the Common Voice 11 dataset has sufficient data in your language to 
+fine-tune your model, you can skip to the next section [Fine-Tune Whisper](#fine-tune-whisper).
+
+### Mixing Datasets (optional)
+
+If the Common Voice 11 dataset contains insufficient training data to fine-tune Whisper in your language, you can explore mixing 
+different datasets to create a larger combined training set. Incorporating supplementary training data is almost always beneficial for training. 
+The Whisper paper demonstrates the significant effect that increasing the amount of training data can have on downstream 
+performance (_c.f._ Section 4.2 of the [paper](https://cdn.openai.com/papers/whisper.pdf)). There are a number of datasets 
+that are available on the Hugging Face Hub that can be downloaded via the 🤗 Datasets library in much the same way as 
+Common Voice 11.
+
+We recommend selecting from the following four datasets on the Hugging Face Hub for multilingual speech recognition:
+
+| Dataset                                                                                       | Languages | Casing | Punctuation |
+|-----------------------------------------------------------------------------------------------|-----------|--------|-------------|
+| [Common Voice 11](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0)       | 100+      | ✅      | ✅           |
+| [VoxPopuli](https://huggingface.co/datasets/facebook/voxpopuli)                               | 15        | ❌      | ✅           |
+| [Multilingual LibriSpeech](https://huggingface.co/datasets/facebook/multilingual_librispeech) | 6         | ❌      | ❌           |
+| [FLEURS](https://huggingface.co/datasets/google/fleurs)                                       | 100+      | ✅      | ✅           |
+
+
+<!---
+<details>
+<summary>
+
+#### Common Voice 11
+
+</summary>
+
+[Common Voice 11](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) is a crowd-sourced 
+open-licensed speech dataset where speakers record text from Wikipedia in various languages. Since anyone can contribute 
+recordings, there is significant variation in both audio quality and speakers. The audio conditions are challenging, with 
+recording artefacts, accented speech, hesitations, and the presence of foreign words. The transcriptions are both cased 
+and punctuated. As of version 11, there are over 100 languages available, both low and high-resource.
+</details>
+<details>
+<summary>
+
+#### VoxPopuli
+
+</summary>
+
+[VoxPopuli](https://huggingface.co/datasets/facebook/voxpopuli) is a large-scale multilingual speech corpus consisting 
+of data sourced from 2009-2020 European Parliament event recordings. Consequently, it occupies the unique domain of 
+oratory, political speech, largely sourced from non-native speakers. It contains labelled audio-transcription data for 
+15 European languages. The transcriptions are punctuated but not cased.
+</details>
+<details>
+<summary>
+
+#### Multilingual LibriSpeech
+
+</summary>
+
+[Multilingual LibriSpeech](https://huggingface.co/datasets/facebook/multilingual_librispeech) is the multilingual 
+equivalent of the [LibriSpeech ASR](https://huggingface.co/datasets/librispeech_asr) corpus. It comprises a large corpus 
+of read audiobooks taken from the [LibriVox](https://librivox.org/) project, making it a suitable dataset for academic 
+research. It contains data split into eight high-resource languages - English, German, Dutch, Spanish, French, Italian, 
+Portuguese and Polish. The transcriptions are neither punctuated nor cased.
+</details>
+<details>
+<summary>
+
+#### FLEURS
+
+</summary>
+
+[FLEURS](https://huggingface.co/datasets/google/fleurs) (Few-shot Learning Evaluation of Universal Representations of 
+Speech) is a dataset for evaluating speech recognition systems in 102 languages, including many that are classified as 
+'low-resource'. The data is derived from the [FLoRes-101](https://arxiv.org/abs/2106.03193) dataset, a machine 
+translation corpus with 3001 sentence translations from English to 101 other languages. Native speakers are recorded 
+narrating the sentence transcriptions in their native language. The recorded audio data is paired with the sentence 
+transcriptions to yield a multilingual speech recognition over all 101 languages. The training sets contain 
+approximately 10 hours of supervised audio-transcription data per language. Transcriptions come in two formats: un-normalised 
+(`"raw_transcription"`) and normalised (`"transcription"`).
+</details>
+
+The previously mentioned blog post provides a more in-depth explanation of the main English speech recognition, 
+multilingual speech recognition and speech translation datasets on the Hub: [A Complete Guide To Audio Datasets](https://huggingface.co/blog/audio-datasets#a-tour-of-audio-datasets-on-the-hub)  
+
+You can also explore all speech recognition datasets on the Hub to find one suited for your language and needs: [ASR datasets on the Hub](https://huggingface.co/datasets?task_categories=task_categories:automatic-speech-recognition&sort=downloads).
+--->
+
+You can try training on these datasets individually, or mix them to form larger train sets.
+
+When mixing datasets, you should ensure the transcription format is consistent across datasets. For example, if you mix 
+Common Voice 11 (cased + punctuated) with VoxPopuli (un-cased + punctuated), you will need to lower-case **all the text** 
+for both training and evaluation, such that the transcriptions are consistent across training samples (un-cased + punctuated). 
+
+Likewise, if mixing Common Voice 11 (cased + punctuated) with Multilingual LibriSpeech (un-cased + un-punctuated), you 
+should make sure to remove all casing and punctuation in **all the text** for both training and evaluation, such that 
+all transcriptions are un-cased and un-punctuated for all training samples.
+
+Having a mismatch in formatting for different training samples can reduce the final performance of your fine-tuned Whisper 
+model.
+
+If you want to combine multiple datasets for training, you can refer to the code-snippet provided for interleaving 
+datasets with streaming mode: [interleave_streaming_datasets.ipynb](https://github.com/huggingface/community-events/blob/main/whisper-fine-tuning-event/interleave_streaming_datasets.ipynb).
+
+### Custom Data (optional)
+
+In addition to publicly available data on the Hugging Face Hub, participants can also make use of their own audio data 
+for training. When using your own audio data, please make sure that you **are allowed to use the audio data**. For 
+instance, if the audio data is taken from media platforms, such as YouTube, please verify that the media platform and 
+the owner of the data have given their approval to use the audio data in the context of machine learning research. If 
+you are not sure whether the data you want to use has the appropriate licensing, please contact the Hugging Face team 
+on Discord.
+
+<!--- TODO: VB - tutorial for adding own data via audio folder --->
+
+## Fine-Tune Whisper
+
+Throughout the event, participants are encouraged to leverage the official pre-trained [Whisper checkpoints](https://huggingface.co/models?pipeline_tag=automatic-speech-recognition&sort=downloads&search=whisper).
+The Whisper checkpoints come in five configurations of varying model sizes.
+The smallest four are trained on either English-only or multilingual data.
+The largest checkpoint is multilingual only. The checkpoints are summarised in the following table with links to the 
+models on the Hugging Face Hub:
+
+| Size   | Layers | Width | Heads | Parameters | English-only                                         | Multilingual                                      |
+|--------|--------|-------|-------|------------|------------------------------------------------------|---------------------------------------------------|
+| tiny   | 4      | 384   | 6     | 39 M       | [✓](https://huggingface.co/openai/whisper-tiny.en)   | [✓](https://huggingface.co/openai/whisper-tiny)  |
+| base   | 6      | 512   | 8     | 74 M       | [✓](https://huggingface.co/openai/whisper-base.en)   | [✓](https://huggingface.co/openai/whisper-base)   |
+| small  | 12     | 768   | 12    | 244 M      | [✓](https://huggingface.co/openai/whisper-small.en)  | [✓](https://huggingface.co/openai/whisper-small)  |
+| medium | 24     | 1024  | 16    | 769 M      | [✓](https://huggingface.co/openai/whisper-medium.en) | [✓](https://huggingface.co/openai/whisper-medium) |
+| large  | 32     | 1280  | 20    | 1550 M     | x                                                    | [✓](https://huggingface.co/openai/whisper-large)  |
+
+The English-only checkpoints should be used for English speech recognition. For all other languages, one should use the 
+multilingual checkpoints.
+
+We recommend using the tiny model for rapid prototyping. **We advise that the small or medium checkpoints are used for 
+fine-tuning**. These checkpoints achieve comparable performance to the large checkpoint, but can be trained much faster 
+(and hence for much longer!).
+
+A complete guide to Whisper fine-tuning can be found in the blog post: [Fine-Tune Whisper with 🤗 Transformers](https://huggingface.co/blog/fine-tune-whisper).
+While it is not necessary to have read this blog post before fine-tuning Whisper, it is strongly advised to gain 
+familiarity with the fine-tuning code.
+
+There are three ways in which you can execute the fine-tuning code:
+1. [Python Script](#python-script)
+2. [Jupyter Notebook](#jupyter-notebook)
+3. [Google Colab](#google-colab)
+
+1 and 2 are applicable when running on a local GPU or cloud GPU instance (such as on Lambda). 3 applies if you have 
+a Google Colab Pro/Pro+ subscription and want to run training in a Google Colab. The proceeding instructions for running 
+each of these methods are quite lengthy. Feel free to read through each of them to get a better idea for which one you 
+want to use for training. Once you've read through, we advise you pick one method and stick to it!
+
+For the walk-through, we'll assume that we're fine-tuning the Whisper model on Spanish ("es") on the Common Voice 11 
+dataset. We'll point out where you'll need to change variables to run the script for your language of choice.
+
+Before jumping into any training, make sure you've accepted the Common Voice 11 [terms of use](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) 
+on the Hugging Face Hub.
+
+### Python Script
+*Checkout the video tutorial detailing how to fine-tune your whisper model via the CLI* 👉️ [YouTube Video](https://www.youtube.com/playlist?list=PLo2EIpI_JMQuKpnFm1ntcLKP6gq0l0f1Q)
+
+1. **Create a model repository**
+
+The steps for running training with a Python script assume that you are SSH'd into your GPU device and have set up 
+your environment according to the previous section [Set Up an Environment](#set-up-an-environment).
+
+First, we need to create a model repository on the Hugging Face Hub. This repository will contain all the required files 
+to reproduce the training run, alongside model weights, training logs and a README.md card. You can either create a model 
+repository directly on the Hugging Face Hub using the link: https://huggingface.co/new Or, via the CLI. Here, we'll show 
+how to use the CLI. 
+
+Let's pick a name for our fine-tuned Whisper model: *whisper-small-es*. We can run the following command to create a 
+repository under this name.
+
+```bash
+huggingface-cli repo create whisper-small-es
+```
+(change "es" to your language code)
+
+We can now see the model on the Hub, *e.g.* under https://huggingface.co/sanchit-gandhi/whisper-small-es
+
+Let's clone the repository so that we can place our training script and model weights inside:
+
+```bash
+git lfs install
+git clone https://huggingface.co/sanchit-gandhi/whisper-small-es
+```
+
+(be sure to change the repo address to `https://huggingface.co/<your-user-name>/<your-repo-name>`)
+
+We can then enter the repository using the `cd` command:
+
+```bash
+cd whisper-small-es
+```
+
+2. **Add training script and `run` command**
+
+We encourage participants to add all the relevant files for training directly to the model repository. This way, 
+training runs are fully reproducible.
+
+We provide a Python training script for fine-tuning Whisper with 🤗 Datasets' streaming mode: [`run_speech_recognition_seq2seq_streaming.py`](https://github.com/huggingface/community-events/blob/main/whisper-fine-tuning-event/run_speech_recognition_streaming.py)
+This script can be copied to your model repository with the following command:
+
+```bash
+cp ~/community-events/whisper-fine-tuning-event/run_speech_recognition_seq2seq_streaming.py .
+```
+
+This will download a copy of the training script to your model repository.
+
+We can then define the model, training and data arguments for fine-tuning:
+
+```bash
+echo 'python run_speech_recognition_seq2seq_streaming.py \
+	--model_name_or_path="openai/whisper-small" \
+	--dataset_name="mozilla-foundation/common_voice_11_0" \
+	--dataset_config_name="es" \
+	--language="spanish" \
+	--train_split_name="train+validation" \
+	--eval_split_name="test" \
+	--model_index_name="Whisper Small Spanish" \
+	--max_steps="5000" \
+	--output_dir="./" \
+	--per_device_train_batch_size="64" \
+	--per_device_eval_batch_size="32" \
+	--logging_steps="25" \
+	--learning_rate="1e-5" \
+	--warmup_steps="500" \
+	--evaluation_strategy="steps" \
+	--eval_steps="1000" \
+	--save_strategy="steps" \
+	--save_steps="1000" \
+	--generation_max_length="225" \
+	--length_column_name="input_length" \
+	--max_duration_in_seconds="30" \
+	--text_column_name="sentence" \
+	--freeze_feature_encoder="False" \
+	--report_to="tensorboard" \
+	--metric_for_best_model="wer" \
+	--greater_is_better="False" \
+	--load_best_model_at_end \
+	--gradient_checkpointing \
+	--fp16 \
+	--overwrite_output_dir \
+	--do_train \
+	--do_eval \
+	--predict_with_generate \
+	--do_normalize_eval \
+	--streaming \
+	--use_auth_token \
+	--push_to_hub' >> run.sh
+```
+
+Make sure to change the `--dataset_config_name` and `--language` to the correct values for your language! See also how 
+we combine the train and validation splits as `--train_split_name="train+validation"`. This is recommended for low-resource 
+languages (it probably isn't strictly necessary for Spanish, where the `"train"` split for Common Voice 11 contains 
+ample training data). We also assign a `"model_index_name"` - a pretty name that will go on the model card. If you are 
+training on a very small dataset (< 10 hours), it is advisable to disable streaming mode: `--streaming="False"`.
+
+We provide the train/eval batch sizes for the "small" checkpoint fine-tuned on a 1x A100 device. Depending on your device and checkpoint, 
+you might need to lower these values. Refer to the subsection [Recommended Training Configurations](#recommended-training-configurations) 
+for suggested batch-sizes for other devices and checkpoints.
+
+3. **Launch training 🚀**
+
+We recommend running training through a `tmux` session. This means that training won't be interrupted when you close 
+your SSH connection. To start a `tmux` session named `mysession`: 
+
+```bash
+tmux new -s mysession
+```
+(if `tmux` is not installed, you can install it through: `sudo apt-get install tmux`)
+
+Once in the `tmux` session, we can launch training:
+
+```bash
+bash run.sh
+```
+
+Training should take approximately 8 hours, with a final cross-entropy loss of **1e-4** and word error rate of **32.6%**.
+
+Since we're in a `tmux` session, we're free to close our SSH window without stopping training!
+
+If you close your SSH connection and want to rejoin the `tmux` window, you can SSH into your GPU and then connect to 
+your session with the following command:
+
+```bash
+tmux a -t mysession
+```
+
+It will be like you never left! 
+
+`tmux` guide: https://www.hamvocke.com/blog/a-quick-and-easy-guide-to-tmux/
+
+### Jupyter Notebook
+*We've detailed these steps in a video tutorial to help you get up to speed faster* 👉️ [YouTube Video](https://www.youtube.com/playlist?list=PLo2EIpI_JMQs9z-N4v8L_Jb4KF6kAkylX)
+
+1. **SSH port forwarding**
+
+The steps for running training with a Python script assume that you have set up your environment according to the 
+previous section [Set Up an Environment](#set-up-an-environment) and are **not** SSH'd into your GPU device. If you are 
+SSH'd into your GPU device, you can close this SSH window and start from your local machine.
+
+The command to SSH into our GPU looked something as follows:
+
+```bash
+ssh ubuntu@104.171.202.236
+```
+
+When running a Jupyter Notebook, we need to "forward" the SSH port from the remote port to the local one. This amounts 
+to adding `-L 8888:localhost:8888` to the end of our SSH command. We can SSH into our remote machine using this modified 
+SSH command:
+
+```bash
+ssh ubuntu@104.171.202.236 -L 8888:localhost:8888
+```
+
+Be sure to change the `ssh ubuntu@...` part to your corresponding SSH command, it's simply the `-L 8888:localhost:8888` 
+part added onto the end that is new. If you want to find out more about SSH port forwarding, we recommend you read the guide: 
+[SSH/OpenSSH/PortForwarding](https://help.ubuntu.com/community/SSH/OpenSSH/PortForwarding).
+
+2. **Create a model repository (copied from previous subsection [Python Script](#python-script))**
+
+First, we need to create a model repository on the Hugging Face Hub. This repository will contain all the required files 
+to reproduce the training run, alongside model weights, training logs and a README.md card. 
+
+You can either create a model repository directly on the Hugging Face Hub using the link: https://huggingface.co/new
+Or, via the CLI. Here, we'll show how to use the CLI. 
+
+Let's pick a name for our fine-tuned Whisper model: *whisper-small-es*. We can run the following command to create a 
+repository under this name.
+
+```bash
+huggingface-cli repo create whisper-small-es
+```
+(change "es" to your language code)
+
+We can now see the model on the Hub, *e.g.* under https://huggingface.co/sanchit-gandhi/whisper-small-es
+
+Let's clone the repository so that we can place our training script and model weights inside:
+
+```bash
+git lfs install
+git clone https://huggingface.co/sanchit-gandhi/whisper-small-es
+```
+
+(be sure to change the repo address to `https://huggingface.co/<your-user-name>/<your-repo-name>`)
+
+We can then enter the repository using the `cd` command:
+
+```bash
+cd whisper-small-es
+```
+
+3. **Add notebook**
+
+We encourage participants to add all the training notebook directly to the model repository. This way, 
+training runs are fully reproducible.
+
+We provide an iPython notebook for fine-tuning Whisper with 🤗 Datasets' streaming mode: [`fine-tune-whisper-streaming.ipynb`](https://github.com/huggingface/community-events/blob/main/whisper-fine-tuning-event/fine-tune-whisper-streaming.ipynb)
+This notebook can be copied to your model repository with the following command:
+
+```bash
+cp ~/community-events/whisper-fine-tuning-event/fine-tune-whisper-streaming.ipynb .
+```
+
+If you are fine-tuning Whisper on a very small dataset (< 10 hours), it is advised that you use the non-streaming notebook 
+[`fine-tune-whisper-non-streaming.ipynb`](https://github.com/huggingface/community-events/blob/main/whisper-fine-tuning-event/fine-tune-whisper-non-streaming.ipynb) 
+(see section [Streaming Mode](#streaming-mode)). This notebook can be copied to your model repository with the following 
+command:
+
+```bash
+cp ~/community-events/whisper-fine-tuning-event/fine-tune-whisper-non-streaming.ipynb .
+```
+
+4. **Launch Jupyter**
+
+First, we need to make sure `jupyterlab` is installed:
+
+```bash
+pip install jupyterlab
+```
+
+We can then link `jupyter lab` to our venv:
+```bash
+python -m ipykernel install --user --name=<your-venv-name>
+```
+
+We recommend running training through a `tmux` session. This means that training won't be interrupted when you close 
+your SSH connection. To start a `tmux` session named `mysession`:
+
+```bash
+tmux new -s mysession
+```
+(if `tmux` is not installed, you can install it through: `sudo apt-get install tmux`)
+
+Once in the `tmux` session, we can launch `jupyter lab`:
+
+```bash
+jupyter lab --port 8888
+```
+
+5. **Open Jupyter in browser**
+
+Now, this is the hardest step of running training from a Jupyter Notebook! Open a second terminal window on your local 
+machine and SSH into your GPU again. This time, it doesn't matter whether we include the `-L 8888:localhost:8888` part, 
+the important thing is that you re-enter your GPU device in a new SSH window.
+
+Once SSH'd into your GPU, view all running `jupyter lab` sessions:
+
+```bash
+jupyter lab list
+```
+
+Copy the URL for the lab corresponding to port 8888 your clipboard, it will take the form `http://localhost:8888/?token=...`. 
+On your local desktop, open a web browser window (Safari, Firefox, Chrome, etc.). Paste the URL into the browser web 
+address bar and press Enter.
+
+Voilà! We're now running a Jupyter Notebook on our GPU machine through the web browser on our local device!
+
+6. **Open fine-tuning notebook**
+
+We can use the file explorer on the left to go to our model repository and open the Jupyter notebook `fine_tune_whisper_streaming.ipynb`. 
+In the top right of the notebook, you'll see a small window that says "Python 3". Clicking on this window will open a 
+dropdown menu, from which we can select a Python kernel. Select your venv from this dropdown menu. This will ensure that 
+you run the notebook in the venv we previously set up.
+
+You can now run this notebook from start to finish and fine-tune the Whisper model as you desire 🤗 The notebook 
+contains pointers for where you need to change variables for your language.
+
+Since we're operating within a `tmux` session, we're free to close our SSH connection and browser window when we desire. 
+Training won't be interrupted by closing this window. However, the notebook will cease to update, so you should make 
+sure that training is working before closing the notebook. You can monitor training progress through your model repo 
+on the Hugging Face Hub under the "Training Metrics" tab.
+
+### Google Colab
+The Google Colab for fine-tuning Whisper is entirely self-contained. No need to set up an environment or sping up a GPU.
+You can access it through the following link:
+
+<a target="_blank" href="https://colab.research.google.com/github/huggingface/community-events/blob/main/whisper-fine-tuning-event/fine_tune_whisper_streaming_colab.ipynb">
+    <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
+</a>
+
+### Recommended Training Configurations
+
+In this section, we provide guidance for appropriate training and evaluation batch sizes depending on your GPU device. 
+Since the Whisper model expects log-Mel input features of a fixed dimension, the GPU memory required by the models is 
+the same for audio samples of any length. Thus, these recommendations should stand for all 16/40GB GPU devices. However, 
+if you experience out-of-memory errors, we recommend reducing the `per_device_train_batch_size` by factors of 2 and 
+increasing the `gradient_accumulation_steps` to compensate.
+
+If you want to explore methods for reducing the memory of the Whisper model, check out the section [Tips and Tricks](#tips-and-tricks).
+
+#### V100 / 16 GB GPU
+
+| Model  | Train Batch Size | Gradient Acc Steps | Eval Batch size |
+|--------|------------------|--------------------|-----------------|
+| small  | 16               | 2                  | 8               |
+| medium | 2                | 16                 | 1               |
+
+It is advised to run the "small" checkpoint if training on a V100 device. Running the medium checkpoint will take 
+upwards of 12 hours for 5k training steps. We reckon you're better off training the "small" checkpoint for longer!
+
+#### A100 / 40GB GPU
+
+| Model  | Train Batch Size | Gradient Acc Steps | Eval Batch size |
+|--------|------------------|--------------------|-----------------|
+| small  | 64               | 1                  | 32              |
+| medium | 32               | 1                  | 16              |
+
+### Punctuation, Casing and Normalisation
+
+When using the Python training script, removing casing for the training data is enabled by passing the flag `--do_lower_case`. 
+Removing punctuation in the training data is achieved by passing the flag `--do_remove_punctuation`. Both of these flags 
+default to False, and we **do not** recommend setting either of them to True. This will ensure your fine-tuned model 
+learns to predict casing and punctuation. Normalisation is only applied during evaluation by setting the flag 
+`--do_normalize_eval` (which defaults to True and recommend setting). Normalisation is performed according to the 
+'official' Whisper normaliser. This normaliser applies the following basic standardisation for non-English text:
+1. Remove any phrases between matching brackets ([, ]).
+2. Remove any phrases between matching parentheses ((, )).
+3. Replace any markers, symbols, and punctuation characters with a space, i.e. when the Unicode category of each character in the NFKC-normalized string starts with M, S, or P.
+4. Make the text lowercase.
+5. Replace any successive whitespace characters with a space.
+
+Similarly, in the notebooks, removing casing in the training data is enabled by setting the variable `do_lower_case = True`, 
+and punctuation by `do_remove_punctuation = True`. We do not recommend setting either of these to True to ensure that 
+your model learns to predict casing and punctuation. Thus, they are set to False by default. Normalisation is only 
+applied during evaluation by setting the variable `do_normalize_eval=True` (which we do recommend setting). 
+
+## Evaluation
+
+We'll be running a live leaderboard throughout the event to track the best performing models across all languages. The leaderboard will track your models performance across *all* the speech recognition models available on the hub for your chosen language and dataset.
+
+You can find the leaderboard [here](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=common_voice_11_0&only_verified=0&task=automatic-speech-recognition&config=th&split=train%2Bvalidation&metric=wer) 📈.
+
+Each participant should evaluate their fine-tuned Whisper checkpoint on the `"test"` split of the Common Voice 11 
+dataset for their respective language. For languages that are not part of the Common Voice 11 dataset, please contact 
+the organisers on Discord so that we can work together to find suitable evaluation data.
+
+We recommend running evaluation during training by setting your eval dataset to the `"test"` split of Common Voice 11.
+We'll also provide you with a standalone evaluation script so that you can test your model after training on Common Voice 
+or other datasets of your choice.
+
+In addition to running evaluation while training, you can noe use your Whisper checkpoints to run evaluation on *any* speech recognition dataset on the hub. The [run_eval_whisper_streaming.py](https://github.com/huggingface/community-events/blob/main/whisper-fine-tuning-event/run_eval_whisper_streaming.py) script loads your whisper checkpoints, runs batch inference on your specified dataset and returns the WER.
+
+You can use the script as follows:
+```bash
+python run_eval_whisper_streaming.py --model_id="openai/whisper-tiny" --dataset="google/fleurs" --config="ar_eg" --device=0 --language="ar"
+```
+
+The evaluation script can be customised with the following parameters:
+1. `model_id` - Whisper model identifier e.g. `openai/whisper-tiny`
+2. `dataset` - Dataset name to evaluate the `model_id` on. Default value: `mozilla-foundation/common_voice_11_0`
+3. `config` - Config of the dataset. e.g. `'en'` for the English split of Common Voice
+4. `split` - Split of the dataset. Default value: `test`
+5. `batch_size` - Number of samples to go through each streamed batch for inference. Default value: `16`
+6. `max_eval_samples` - Max number of samples to be evaluated from the dataset. Put a lower number e.g. `64` for testing this script. **Only use this for testing the script**
+7. `streaming` - Whether you'd like to download the entire dataset or stream it during the evaluation. Default value: `True`
+8. `language` - Language you want the `model_id` to transcribe the audio in.
+9. `device` - The device to run the pipeline on. e.g. `0` for running on GPU 0. Default value: -1 for CPU.
+
+## Building a Demo
+
+Finally, on to the fun part! Time to sit back and watch the model transcribe audio. We've created a [template Gradio demo](https://huggingface.co/spaces/whisper-event/whisper-demo) 
+that you can use to showcase your fine-tuned Whisper model 📢
+
+Click the link to duplicate the template demo to your account: https://huggingface.co/spaces/whisper-event/whisper-demo?duplicate=true
+
+We recommend giving your space a similar name to your fine-tuned model (e.g. `whisper-demo-es`) and setting the visibility 
+to "Public". 
+
+Once you've duplicated the Space to your account, click "Files and versions" -> "app.py" -> "edit". Change the model 
+identifier to your fine-tuned model (line 9). Scroll to the bottom of the page and click "Commit changes to `main`". 
+The demo will reboot, this time using your fine-tuned model. You can share this demo with your friends and family so 
+that they can use the model that you've trained!
+
+*Checkout our video tutorial to get a better understanding 👉️ [YouTube Video](https://www.youtube.com/watch?v=VQYuvl6-9VE)*
+
+## Communication and Problems
+
+If you encounter any problems or have any questions, you should use one of the following platforms
+depending on your type of problem. Hugging Face is an "open-source-first" organisation, meaning 
+that we'll try to solve all problems in the most public and transparent way possible so that everybody
+in the community benefits.
+
+The following paragraph summarises the platform to use for each kind of problem:
+
+- Problem/question/bug with the 🤗 Datasets library that you think is a general problem that also impacts other people, please open an [Issue on Datasets](https://github.com/huggingface/datasets/issues/new?assignees=&labels=bug&template=bug-report.md&title=) and ping @sanchit-gandhi and @vaibhavs10.
+- Problem/question/bug with the 🤗 Transformers library that you think is a general problem that also impacts other people, please open an [Issue on Transformers](https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title=) and ping @sanchit-gandhi and @vaibhavs10.
+- Problem/question with a modified, customised training script that is less likely to impact other people, please post your problem/question [on the forum](https://discuss.huggingface.co/) and ping @sanchit-gandhi and @vaibhavs10.
+- Problem/question regarding access or set up of a Lambda GPU, please ask in the Discord channel **#lambdalabs-infra-support**.
+- Other questions regarding the event, rules of the event, or if you are unsure where to post your question, please ask in the Discord channel **#events**.
+
+## Talks
+
+We are very excited to be hosting talks from Open AI, Meta AI and Hugging Face to help you get a better understanding of the Whisper model, the VoxPopuli dataset and details about the fine-tuning event itself!
+
+| **Speaker**                  | **Topic**                                                  | **Time**                      | **Video**                                                                                                                 |
+|------------------------------|------------------------------------------------------------|-------------------------------|---------------------------------------------------------------------------------------------------------------------------|
+| Sanchit Gandhi, Hugging Face | Introduction to Whisper Fine-Tuning Event                  | 15:00 UTC, 2nd December, 2022 | [![Youtube](https://www.youtube.com/s/desktop/f506bd45/img/favicon_32.png)](https://www.youtube.com/watch?v=1cVBLOMlv3w)  |
+| Jong Wook Kim, OpenAI        | [Whisper Model](https://cdn.openai.com/papers/whisper.pdf) | 16:30 UTC, 5th December, 2022 | [![Youtube](https://www.youtube.com/s/desktop/f506bd45/img/favicon_32.png)](https://www.youtube.com/watch?v=fZMiD8sDzzg ) |
+| Changhan Wang, MetaAI        | [VoxPopuli Dataset](https://arxiv.org/abs/2101.00390)      | 17:30 UTC, 5th December, 2022 | [![Youtube](https://www.youtube.com/s/desktop/f506bd45/img/favicon_32.png)](https://www.youtube.com/watch?v=fZMiD8sDzzg ) |
+
+## Tips and Tricks
+
+We include three memory saving tricks that you can explore to run the fine-tuning scripts with larger batch-sizes and 
+potentially larger checkpoints.
+
+### Adam 8bit
+The [Adam optimiser](https://arxiv.org/abs/1412.6980a) requires two params (betas) for every model parameter. So the memory requirement of the optimiser is 
+**two times** that of the model. You can switch to using an 8bit version of the Adam optimiser from [`bitsandbytes`](https://github.com/TimDettmers/bitsandbytes#bitsandbytes). 
+This will cast the optimiser parameters into 8bit precision, saving you a lot of memory and potentially allowing you to run bigger batch sizes.
+To use Adam 8bit, you first need to pip install `bitsandbytes`:
+
+```bash
+pip install bitsandbytes
+```
+
+Then, set `optim="adamw_bnb_8bit"`, either in your `run.sh` file if running from a Python script, or when you 
+instantiate the Seq2SeqTrainingArguments from a Jupyter Notebook or Google Colab:
+
+```python
+from transformers import Seq2SeqTrainingArguments
+
+training_args = Seq2SeqTrainingArguments(
+    output_dir="./",
+    per_device_train_batch_size=64,
+    gradient_accumulation_steps=1,  # increase by 2x for every 2x decrease in batch size
+    learning_rate=1e-5,
+    warmup_steps=500,
+    max_steps=5000,
+    gradient_checkpointing=True,
+    fp16=True,
+    evaluation_strategy="steps",
+    per_device_eval_batch_size=8,
+    predict_with_generate=True,
+    generation_max_length=225,
+    save_steps=1000,
+    eval_steps=1000,
+    logging_steps=25,
+    report_to=["tensorboard"],
+    load_best_model_at_end=True,
+    metric_for_best_model="wer",
+    greater_is_better=False,
+    push_to_hub=True,
+    optim="adamw_bnb_8bit"
+)
+```
+
+### Adafactor
+
+Rather than using Adam, you can use a different optimiser all together. Adam requires two optimiser params per one model 
+param, but [Adafactor](https://arxiv.org/abs/1804.04235) uses only one. To enable Adafactor, set `optim="adafactor"` in the 
+`Seq2SeqTrainingArguments`. You can expect to double your training batch size when using Adafactor compared to Adam. 
+
+A word of caution: Adafactor is untested for fine-tuning Whisper, so we are unsure sure how 
+Adafactor performance compares to Adam! Typically, using Adafactor results in **slower convergence** than using Adam or 
+Adam 8bit. For this reason, we recommend Adafactor as an **experimental feature** only.
+
+### DeepSpeed
+
+DeepSpeed is a framework for training larger deep learning models with limited GPU resources by optimising GPU utilisation. 
+We provide implementation details for DeepSpeed ZeRo Stage 2, which partitions the optimiser states (ZeRO stage 1) and gradients 
+(ZeRO stage 2). With DeepSpeed, it is more than possible to train the medium Whisper checkpoint on a V100, or the large 
+checkpoint on an A100. For more details, we refer you to the blog post by the original authors: [DeepSpeed ZeRO](https://www.microsoft.com/en-us/research/blog/zero-deepspeed-new-system-optimizations-enable-training-models-with-over-100-billion-parameters/).
+
+Using DeepSpeed with 🤗 Transformers is straightforward. First, we need to install the packages 🤗 Accelerate and DeepSpeed: 
+
+```bash
+pip install -U accelerate deepspeed
+```
+
+The DeepSpeed configuration file specifies precisely what form of optimiser/gradient offloading we are going to perform.
+The key to getting a huge improvement on a single GPU with DeepSpeed is to have at least the provided DeepSpeed configuration 
+in the configuration file [`ds_config.json`](https://github.com/huggingface/community-events/blob/main/whisper-fine-tuning-event/ds_config.json). 
+
+You can copy the DeepSpeed configuration file to your model repository as follows:
+
+```bash
+cp ~/community-events/whisper-fine-tuning-event/ds_config.json .
+```
+
+### Python Script
+
+Using DeepSpeed with the Python training script requires two changes to the `run.sh` file. Firstly, we launch the script using `deepspeed` 
+instead of Python. Secondly, we pass the DeepSpeed config `ds_config.json` as a training argument. The remainder of the `run.sh` 
+file takes the same format as using the native Trainer configuration:
+
+```bash
+deepspeed run_speech_recognition_seq2seq_streaming.py \
+	--deepspeed="ds_config.json" \
+	--model_name_or_path="openai/whisper-small" \
+	--dataset_name="mozilla-foundation/common_voice_11_0" \
+	--dataset_config_name="es" \
+	--language="spanish" \
+	--train_split_name="train+validation" \
+	--eval_split_name="test" \
+	--model_index_name="Whisper Small Spanish" \
+	--max_steps="5000" \
+	--output_dir="./" \
+	--per_device_train_batch_size="64" \
+	--per_device_eval_batch_size="32" \
+	--logging_steps="25" \
+	--learning_rate="1e-5" \
+	--warmup_steps="500" \
+	--evaluation_strategy="steps" \
+	--eval_steps="1000" \
+	--save_strategy="steps" \
+	--save_steps="1000" \
+	--generation_max_length="225" \
+	--length_column_name="input_length" \
+	--max_duration_in_seconds="30" \
+	--text_column_name="sentence" \
+	--freeze_feature_encoder="False" \
+	--report_to="tensorboard" \
+	--metric_for_best_model="wer" \
+	--greater_is_better="False" \
+	--load_best_model_at_end \
+	--gradient_checkpointing \
+	--fp16 \
+	--overwrite_output_dir \
+	--do_train \
+	--do_eval \
+	--predict_with_generate \
+	--do_normalize_eval \
+	--streaming \
+	--use_auth_token \
+	--push_to_hub
+```
+
+### Jupyter Notebook
+
+Using DeepSpeed with the template Jupyter Notebooks requires two changes. Firstly, we add the following code cell at the 
+start of the notebook to configure the DeepSpeed environment:
+
+```python
+# DeepSpeed requires a distributed environment even when only one process is used.
+# This emulates a launcher in the notebook
+import os
+
+os.environ["MASTER_ADDR"] = "localhost"
+os.environ["MASTER_PORT"] = "9994"  # modify if RuntimeError: Address already in use
+os.environ["RANK"] = "0"
+os.environ["LOCAL_RANK"] = "0"
+os.environ["WORLD_SIZE"] = "1"
+```
+
+Secondly, we pass the DeepSpeed config file to the training args:
+
+```python
+training_args = Seq2SeqTrainingArguments(..., deepspeed="ds_config.json")
+```
+
+### Recommended Batch Sizes with DeepSpeed
+
+Using DeepSpeed, it is possible to fit larger batch sizes and even larger checkpoints on your device, be it a V100 or 
+A100. We provide recommended batch sizes for the three checkpoint sizes of interest for 16GB GPUs and 40GB GPUs. As before,
+these batch sizes are only indicative: you should tune the batch size depending on your device, checkpoint and language.
+
+#### V100 / 16 GB GPU
+
+| Model  | Train Batch Size | Gradient Acc Steps | Eval Batch size | Speed   |
+|--------|------------------|--------------------|-----------------|---------|
+| small  | 32               | 1                  | 16              | 1.3s/it |
+| medium | 16               | 1 or 2             | 8               | 2.0s/it |
+| large  | 8                | 2 or 4             | 4               | 3.8s/it |
+
+#### A100 / 40GB GPU
+
+| Model  | Train Batch Size | Gradient Acc Steps | Eval Batch size | Speed   |
+|--------|------------------|--------------------|-----------------|---------|
+| small  | 64               | 1                  | 32              | 2.3s/it |
+| medium | 64               | 1                  | 32              | 5.8s/it |
+| large  | 32               | 1 or 2             | 16              | 5.9s/it |
+
+
+## Scripts & Colabs
+
+1. [Whirlwind tour of Whispering with 🤗Transformers](https://colab.research.google.com/drive/1l290cRv4RdvuLNlSeo9WexByHaNWs3s3?usp=sharing)
+2. [8bit inference for Whisper large model (6.5 gig VRAM) 🤯](https://colab.research.google.com/drive/1EMOwwfm1V1fHxH7eT1LLg7yBjhTooB6j?usp=sharing)
+
+<!--- TODO: VB - Move these colabs to a GitHub repo --->
+
+## Feedback
+
+We would love to get your feedback on the event! If you have a spare ten minutes, we'd appreciate you filling out the 
+feedback form at: https://forms.gle/7hvrTE8NaSdQwwU68
diff --git a/whisper-fine-tuning-event/added_tokens.json b/whisper-fine-tuning-event/added_tokens.json
new file mode 100644
index 0000000000000000000000000000000000000000..e3d256c988462aa153dcabe2aa38b8e9b436c06f
--- /dev/null
+++ b/whisper-fine-tuning-event/added_tokens.json
@@ -0,0 +1,1609 @@
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+}
diff --git a/whisper-fine-tuning-event/checkpoint-1000/config.json b/whisper-fine-tuning-event/checkpoint-1000/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..8d2a327cb177d5048125acef1f6c6fbab0b47606
--- /dev/null
+++ b/whisper-fine-tuning-event/checkpoint-1000/config.json
@@ -0,0 +1,52 @@
+{
+  "_name_or_path": "openai/whisper-small",
+  "activation_dropout": 0.0,
+  "activation_function": "gelu",
+  "apply_spec_augment": false,
+  "architectures": [
+    "WhisperForConditionalGeneration"
+  ],
+  "attention_dropout": 0.0,
+  "begin_suppress_tokens": [
+    220,
+    50257
+  ],
+  "bos_token_id": 50257,
+  "classifier_proj_size": 256,
+  "d_model": 768,
+  "decoder_attention_heads": 12,
+  "decoder_ffn_dim": 3072,
+  "decoder_layerdrop": 0.0,
+  "decoder_layers": 12,
+  "decoder_start_token_id": 50258,
+  "dropout": 0.0,
+  "encoder_attention_heads": 12,
+  "encoder_ffn_dim": 3072,
+  "encoder_layerdrop": 0.0,
+  "encoder_layers": 12,
+  "eos_token_id": 50257,
+  "forced_decoder_ids": null,
+  "init_std": 0.02,
+  "is_encoder_decoder": true,
+  "mask_feature_length": 10,
+  "mask_feature_min_masks": 0,
+  "mask_feature_prob": 0.0,
+  "mask_time_length": 10,
+  "mask_time_min_masks": 2,
+  "mask_time_prob": 0.05,
+  "max_length": 448,
+  "max_source_positions": 1500,
+  "max_target_positions": 448,
+  "median_filter_width": 7,
+  "model_type": "whisper",
+  "num_hidden_layers": 12,
+  "num_mel_bins": 80,
+  "pad_token_id": 50257,
+  "scale_embedding": false,
+  "suppress_tokens": [],
+  "torch_dtype": "float32",
+  "transformers_version": "4.40.0.dev0",
+  "use_cache": false,
+  "use_weighted_layer_sum": false,
+  "vocab_size": 51865
+}
diff --git a/whisper-fine-tuning-event/checkpoint-1000/generation_config.json b/whisper-fine-tuning-event/checkpoint-1000/generation_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..5576849bec3fa8e898478b36fc743acda59479f3
--- /dev/null
+++ b/whisper-fine-tuning-event/checkpoint-1000/generation_config.json
@@ -0,0 +1,265 @@
+{
+  "alignment_heads": [
+    [
+      5,
+      3
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+      "eval_runtime": 1476.388,
+      "eval_samples_per_second": 1.96,
+      "eval_steps_per_second": 0.49,
+      "eval_wer": 18.498763849464332,
+      "step": 5000
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+  ],
+  "logging_steps": 25,
+  "max_steps": 5000,
+  "num_input_tokens_seen": 0,
+  "num_train_epochs": 50,
+  "save_steps": 1000,
+  "total_flos": 9.229191970553856e+19,
+  "train_batch_size": 8,
+  "trial_name": null,
+  "trial_params": null
+}
diff --git a/whisper-fine-tuning-event/checkpoint-5000/training_args.bin b/whisper-fine-tuning-event/checkpoint-5000/training_args.bin
new file mode 100644
index 0000000000000000000000000000000000000000..6f1e1c1503977a74c2be326ce97dee50daa53384
--- /dev/null
+++ b/whisper-fine-tuning-event/checkpoint-5000/training_args.bin
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+version https://git-lfs.github.com/spec/v1
+oid sha256:4f73c95d6632bb7d80507c5d129a813cefcc6575c685b8993420525612405d91
+size 5048
diff --git a/whisper-fine-tuning-event/config.json b/whisper-fine-tuning-event/config.json
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+++ b/whisper-fine-tuning-event/config.json
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+{
+  "_name_or_path": "openai/whisper-small",
+  "activation_dropout": 0.0,
+  "activation_function": "gelu",
+  "apply_spec_augment": false,
+  "architectures": [
+    "WhisperForConditionalGeneration"
+  ],
+  "attention_dropout": 0.0,
+  "begin_suppress_tokens": [
+    220,
+    50257
+  ],
+  "bos_token_id": 50257,
+  "classifier_proj_size": 256,
+  "d_model": 768,
+  "decoder_attention_heads": 12,
+  "decoder_ffn_dim": 3072,
+  "decoder_layerdrop": 0.0,
+  "decoder_layers": 12,
+  "decoder_start_token_id": 50258,
+  "dropout": 0.0,
+  "encoder_attention_heads": 12,
+  "encoder_ffn_dim": 3072,
+  "encoder_layerdrop": 0.0,
+  "encoder_layers": 12,
+  "eos_token_id": 50257,
+  "forced_decoder_ids": null,
+  "init_std": 0.02,
+  "is_encoder_decoder": true,
+  "mask_feature_length": 10,
+  "mask_feature_min_masks": 0,
+  "mask_feature_prob": 0.0,
+  "mask_time_length": 10,
+  "mask_time_min_masks": 2,
+  "mask_time_prob": 0.05,
+  "max_length": 448,
+  "max_source_positions": 1500,
+  "max_target_positions": 448,
+  "median_filter_width": 7,
+  "model_type": "whisper",
+  "num_hidden_layers": 12,
+  "num_mel_bins": 80,
+  "pad_token_id": 50257,
+  "scale_embedding": false,
+  "suppress_tokens": [],
+  "torch_dtype": "float32",
+  "transformers_version": "4.40.0.dev0",
+  "use_cache": false,
+  "use_weighted_layer_sum": false,
+  "vocab_size": 51865
+}
diff --git a/whisper-fine-tuning-event/ds_config.json b/whisper-fine-tuning-event/ds_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..6fe6c2429adbd7c17f25d9ba41613262ab860549
--- /dev/null
+++ b/whisper-fine-tuning-event/ds_config.json
@@ -0,0 +1,50 @@
+{
+    "fp16": {
+        "enabled": "auto",
+        "loss_scale": 0,
+        "loss_scale_window": 1000,
+        "initial_scale_power": 16,
+        "hysteresis": 2,
+        "min_loss_scale": 1
+    },
+
+    "optimizer": {
+        "type": "AdamW",
+        "params": {
+            "lr": "auto",
+            "betas": "auto",
+            "eps": "auto",
+            "weight_decay": "auto"
+        }
+    },
+
+   "scheduler": {
+         "type": "WarmupDecayLR",
+         "params": {
+             "last_batch_iteration": -1,
+             "total_num_steps": "auto",
+             "warmup_min_lr": "auto",
+             "warmup_max_lr": "auto",
+             "warmup_num_steps": "auto"
+         }
+     },
+
+    "zero_optimization": {
+        "stage": 2,
+        "offload_optimizer": {
+            "device": "cpu",
+            "pin_memory": true
+        },
+        "allgather_partitions": true,
+        "allgather_bucket_size": 2e8,
+        "overlap_comm": true,
+        "reduce_scatter": true,
+        "reduce_bucket_size": 2e8,
+        "contiguous_gradients": true
+    },
+
+    "gradient_accumulation_steps": "auto",
+    "gradient_clipping": "auto",
+    "train_batch_size": "auto",
+    "train_micro_batch_size_per_gpu": "auto"
+}
diff --git a/whisper-fine-tuning-event/fine-tune-whisper-non-streaming.ipynb b/whisper-fine-tuning-event/fine-tune-whisper-non-streaming.ipynb
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index 0000000000000000000000000000000000000000..33f9847de5ffa4741781b7f0a1c415b0148e63d4
--- /dev/null
+++ b/whisper-fine-tuning-event/fine-tune-whisper-non-streaming.ipynb
@@ -0,0 +1,15057 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "75b58048-7d14-4fc6-8085-1fc08c81b4a6",
+   "metadata": {
+    "id": "75b58048-7d14-4fc6-8085-1fc08c81b4a6"
+   },
+   "source": [
+    "# Fine-Tune Whisper For Multilingual ASR with 🤗 Transformers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbfa8ad5-4cdc-4512-9058-836cbbf65e1a",
+   "metadata": {
+    "id": "fbfa8ad5-4cdc-4512-9058-836cbbf65e1a"
+   },
+   "source": [
+    "In this Colab, we present a step-by-step guide on how to fine-tune Whisper \n",
+    "for any multilingual ASR dataset using Hugging Face 🤗 Transformers. This is a \n",
+    "more \"hands-on\" version of the accompanying [blog post](https://huggingface.co/blog/fine-tune-whisper). \n",
+    "For a more in-depth explanation of Whisper, the Common Voice dataset and the theory behind fine-tuning, the reader is advised to refer to the blog post."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "afe0d503-ae4e-4aa7-9af4-dbcba52db41e",
+   "metadata": {
+    "id": "afe0d503-ae4e-4aa7-9af4-dbcba52db41e"
+   },
+   "source": [
+    "## Introduction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ae91ed4-9c3e-4ade-938e-f4c2dcfbfdc0",
+   "metadata": {
+    "id": "9ae91ed4-9c3e-4ade-938e-f4c2dcfbfdc0"
+   },
+   "source": [
+    "Whisper is a pre-trained model for automatic speech recognition (ASR) \n",
+    "published in [September 2022](https://openai.com/blog/whisper/) by the authors \n",
+    "Alec Radford et al. from OpenAI. Unlike many of its predecessors, such as \n",
+    "[Wav2Vec 2.0](https://arxiv.org/abs/2006.11477), which are pre-trained \n",
+    "on un-labelled audio data, Whisper is pre-trained on a vast quantity of \n",
+    "**labelled** audio-transcription data, 680,000 hours to be precise. \n",
+    "This is an order of magnitude more data than the un-labelled audio data used \n",
+    "to train Wav2Vec 2.0 (60,000 hours). What is more, 117,000 hours of this \n",
+    "pre-training data is multilingual ASR data. This results in checkpoints \n",
+    "that can be applied to over 96 languages, many of which are considered \n",
+    "_low-resource_.\n",
+    "\n",
+    "When scaled to 680,000 hours of labelled pre-training data, Whisper models \n",
+    "demonstrate a strong ability to generalise to many datasets and domains.\n",
+    "The pre-trained checkpoints achieve competitive results to state-of-the-art \n",
+    "ASR systems, with near 3% word error rate (WER) on the test-clean subset of \n",
+    "LibriSpeech ASR and a new state-of-the-art on TED-LIUM with 4.7% WER (_c.f._ \n",
+    "Table 8 of the [Whisper paper](https://cdn.openai.com/papers/whisper.pdf)).\n",
+    "The extensive multilingual ASR knowledge acquired by Whisper during pre-training \n",
+    "can be leveraged for other low-resource languages; through fine-tuning, the \n",
+    "pre-trained checkpoints can be adapted for specific datasets and languages \n",
+    "to further improve upon these results. We'll show just how Whisper can be fine-tuned \n",
+    "for low-resource languages in this Colab."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e59b91d6-be24-4b5e-bb38-4977ea143a72",
+   "metadata": {
+    "id": "e59b91d6-be24-4b5e-bb38-4977ea143a72"
+   },
+   "source": [
+    "<figure>\n",
+    "<img src=\"https://raw.githubusercontent.com/sanchit-gandhi/notebooks/main/whisper_architecture.svg\" alt=\"Trulli\" style=\"width:100%\">\n",
+    "<figcaption align = \"center\"><b>Figure 1:</b> Whisper model. The architecture \n",
+    "follows the standard Transformer-based encoder-decoder model. A \n",
+    "log-Mel spectrogram is input to the encoder. The last encoder \n",
+    "hidden states are input to the decoder via cross-attention mechanisms. The \n",
+    "decoder autoregressively predicts text tokens, jointly conditional on the \n",
+    "encoder hidden states and previously predicted tokens. Figure source: \n",
+    "<a href=\"https://openai.com/blog/whisper/\">OpenAI Whisper Blog</a>.</figcaption>\n",
+    "</figure>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21b6316e-8a55-4549-a154-66d3da2ab74a",
+   "metadata": {
+    "id": "21b6316e-8a55-4549-a154-66d3da2ab74a"
+   },
+   "source": [
+    "The Whisper checkpoints come in five configurations of varying model sizes.\n",
+    "The smallest four are trained on either English-only or multilingual data.\n",
+    "The largest checkpoint is multilingual only. All nine of the pre-trained checkpoints \n",
+    "are available on the [Hugging Face Hub](https://huggingface.co/models?search=openai/whisper). The \n",
+    "checkpoints are summarised in the following table with links to the models on the Hub:\n",
+    "\n",
+    "| Size   | Layers | Width | Heads | Parameters | English-only                                         | Multilingual                                      |\n",
+    "|--------|--------|-------|-------|------------|------------------------------------------------------|---------------------------------------------------|\n",
+    "| tiny   | 4      | 384   | 6     | 39 M       | [✓](https://huggingface.co/openai/whisper-tiny.en)   | [✓](https://huggingface.co/openai/whisper-tiny.)  |\n",
+    "| base   | 6      | 512   | 8     | 74 M       | [✓](https://huggingface.co/openai/whisper-base.en)   | [✓](https://huggingface.co/openai/whisper-base)   |\n",
+    "| small  | 12     | 768   | 12    | 244 M      | [✓](https://huggingface.co/openai/whisper-small.en)  | [✓](https://huggingface.co/openai/whisper-small)  |\n",
+    "| medium | 24     | 1024  | 16    | 769 M      | [✓](https://huggingface.co/openai/whisper-medium.en) | [✓](https://huggingface.co/openai/whisper-medium) |\n",
+    "| large  | 32     | 1280  | 20    | 1550 M     | x                                                    | [✓](https://huggingface.co/openai/whisper-large)  |\n",
+    "\n",
+    "For demonstration purposes, we'll fine-tune the multilingual version of the \n",
+    "[`\"small\"`](https://huggingface.co/openai/whisper-small) checkpoint with 244M params (~= 1GB). \n",
+    "As for our data, we'll train and evaluate our system on a low-resource language \n",
+    "taken from the [Common Voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0)\n",
+    "dataset. We'll show that with as little as 8 hours of fine-tuning data, we can achieve \n",
+    "strong performance in this language."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a680dfc-cbba-4f6c-8a1f-e1a5ff3f123a",
+   "metadata": {
+    "id": "3a680dfc-cbba-4f6c-8a1f-e1a5ff3f123a"
+   },
+   "source": [
+    "------------------------------------------------------------------------\n",
+    "\n",
+    "\\\\({}^1\\\\) The name Whisper follows from the acronym “WSPSR”, which stands for “Web-scale Supervised Pre-training for Speech Recognition”."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b219c9dd-39b6-4a95-b2a1-3f547a1e7bc0",
+   "metadata": {
+    "id": "b219c9dd-39b6-4a95-b2a1-3f547a1e7bc0"
+   },
+   "source": [
+    "## Load Dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "674429c5-0ab4-4adf-975b-621bb69eca38",
+   "metadata": {
+    "id": "674429c5-0ab4-4adf-975b-621bb69eca38"
+   },
+   "source": [
+    "Using 🤗 Datasets, downloading and preparing data is extremely simple. \n",
+    "We can download and prepare the Common Voice splits in just one line of code. \n",
+    "\n",
+    "First, ensure you have accepted the terms of use on the Hugging Face Hub: [mozilla-foundation/common_voice_11_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0). Once you have accepted the terms, you will have full access to the dataset and be able to download the data locally.\n",
+    "\n",
+    "Since Hindi is very low-resource, we'll combine the `train` and `validation` \n",
+    "splits to give approximately 8 hours of training data. We'll use the 4 hours \n",
+    "of `test` data as our held-out test set:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a2787582-554f-44ce-9f38-4180a5ed6b44",
+   "metadata": {
+    "id": "a2787582-554f-44ce-9f38-4180a5ed6b44"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/datasets/load.py:1483: FutureWarning: The repository for mozilla-foundation/common_voice_11_0 contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/mozilla-foundation/common_voice_11_0\n",
+      "You can avoid this message in future by passing the argument `trust_remote_code=True`.\n",
+      "Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/datasets/load.py:1483: FutureWarning: The repository for mozilla-foundation/common_voice_11_0 contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/mozilla-foundation/common_voice_11_0\n",
+      "You can avoid this message in future by passing the argument `trust_remote_code=True`.\n",
+      "Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DatasetDict({\n",
+      "    train: Dataset({\n",
+      "        features: ['client_id', 'path', 'audio', 'sentence', 'up_votes', 'down_votes', 'age', 'gender', 'accent', 'locale', 'segment'],\n",
+      "        num_rows: 6540\n",
+      "    })\n",
+      "    test: Dataset({\n",
+      "        features: ['client_id', 'path', 'audio', 'sentence', 'up_votes', 'down_votes', 'age', 'gender', 'accent', 'locale', 'segment'],\n",
+      "        num_rows: 2894\n",
+      "    })\n",
+      "})\n"
+     ]
+    }
+   ],
+   "source": [
+    "from datasets import load_dataset, DatasetDict\n",
+    "\n",
+    "common_voice = DatasetDict()\n",
+    "\n",
+    "common_voice[\"train\"] = load_dataset(\"mozilla-foundation/common_voice_11_0\", \"hi\", split=\"train+validation\", token=True)\n",
+    "common_voice[\"test\"] = load_dataset(\"mozilla-foundation/common_voice_11_0\", \"hi\", split=\"test\", token=True)\n",
+    "\n",
+    "print(common_voice)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d5c7c3d6-7197-41e7-a088-49b753c1681f",
+   "metadata": {
+    "id": "d5c7c3d6-7197-41e7-a088-49b753c1681f"
+   },
+   "source": [
+    "Most ASR datasets only provide input audio samples (`audio`) and the \n",
+    "corresponding transcribed text (`sentence`). Common Voice contains additional \n",
+    "metadata information, such as `accent` and `locale`, which we can disregard for ASR.\n",
+    "Keeping the notebook as general as possible, we only consider the input audio and\n",
+    "transcribed text for fine-tuning, discarding the additional metadata information:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "20ba635d-518c-47ac-97ee-3cad25f1e0ce",
+   "metadata": {
+    "id": "20ba635d-518c-47ac-97ee-3cad25f1e0ce"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DatasetDict({\n",
+      "    train: Dataset({\n",
+      "        features: ['audio', 'sentence'],\n",
+      "        num_rows: 6540\n",
+      "    })\n",
+      "    test: Dataset({\n",
+      "        features: ['audio', 'sentence'],\n",
+      "        num_rows: 2894\n",
+      "    })\n",
+      "})\n"
+     ]
+    }
+   ],
+   "source": [
+    "common_voice = common_voice.remove_columns([\"accent\", \"age\", \"client_id\", \"down_votes\", \"gender\", \"locale\", \"path\", \"segment\", \"up_votes\"])\n",
+    "\n",
+    "print(common_voice)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d63b2d2-f68a-4d74-b7f1-5127f6d16605",
+   "metadata": {
+    "id": "2d63b2d2-f68a-4d74-b7f1-5127f6d16605"
+   },
+   "source": [
+    "## Prepare Feature Extractor, Tokenizer and Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "601c3099-1026-439e-93e2-5635b3ba5a73",
+   "metadata": {
+    "id": "601c3099-1026-439e-93e2-5635b3ba5a73"
+   },
+   "source": [
+    "The ASR pipeline can be de-composed into three stages: \n",
+    "1) A feature extractor which pre-processes the raw audio-inputs\n",
+    "2) The model which performs the sequence-to-sequence mapping \n",
+    "3) A tokenizer which post-processes the model outputs to text format\n",
+    "\n",
+    "In 🤗 Transformers, the Whisper model has an associated feature extractor and tokenizer, \n",
+    "called [WhisperFeatureExtractor](https://huggingface.co/docs/transformers/main/model_doc/whisper#transformers.WhisperFeatureExtractor)\n",
+    "and [WhisperTokenizer](https://huggingface.co/docs/transformers/main/model_doc/whisper#transformers.WhisperTokenizer) \n",
+    "respectively.\n",
+    "\n",
+    "We'll go through details for setting-up the feature extractor and tokenizer one-by-one!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "560332eb-3558-41a1-b500-e83a9f695f84",
+   "metadata": {
+    "id": "560332eb-3558-41a1-b500-e83a9f695f84"
+   },
+   "source": [
+    "### Load WhisperFeatureExtractor"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32ec8068-0bd7-412d-b662-0edb9d1e7365",
+   "metadata": {
+    "id": "32ec8068-0bd7-412d-b662-0edb9d1e7365"
+   },
+   "source": [
+    "The Whisper feature extractor performs two operations:\n",
+    "1. Pads / truncates the audio inputs to 30s: any audio inputs shorter than 30s are padded to 30s with silence (zeros), and those longer that 30s are truncated to 30s\n",
+    "2. Converts the audio inputs to _log-Mel spectrogram_ input features, a visual representation of the audio and the form of the input expected by the Whisper model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "589d9ec1-d12b-4b64-93f7-04c63997da19",
+   "metadata": {
+    "id": "589d9ec1-d12b-4b64-93f7-04c63997da19"
+   },
+   "source": [
+    "<figure>\n",
+    "<img src=\"https://raw.githubusercontent.com/sanchit-gandhi/notebooks/main/spectrogram.jpg\" alt=\"Trulli\" style=\"width:100%\">\n",
+    "<figcaption align = \"center\"><b>Figure 2:</b> Conversion of sampled audio array to log-Mel spectrogram.\n",
+    "Left: sampled 1-dimensional audio signal. Right: corresponding log-Mel spectrogram. Figure source:\n",
+    "<a href=\"https://ai.googleblog.com/2019/04/specaugment-new-data-augmentation.html\">Google SpecAugment Blog</a>.\n",
+    "</figcaption>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2ef54d5-b946-4c1d-9fdc-adc5d01b46aa",
+   "metadata": {
+    "id": "b2ef54d5-b946-4c1d-9fdc-adc5d01b46aa"
+   },
+   "source": [
+    "We'll load the feature extractor from the pre-trained checkpoint with the default values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "bc77d7bb-f9e2-47f5-b663-30f7a4321ce5",
+   "metadata": {
+    "id": "bc77d7bb-f9e2-47f5-b663-30f7a4321ce5"
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import WhisperFeatureExtractor\n",
+    "\n",
+    "feature_extractor = WhisperFeatureExtractor.from_pretrained(\"openai/whisper-small\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93748af7-b917-4ecf-a0c8-7d89077ff9cb",
+   "metadata": {
+    "id": "93748af7-b917-4ecf-a0c8-7d89077ff9cb"
+   },
+   "source": [
+    "### Load WhisperTokenizer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2bc82609-a9fb-447a-a2af-99597c864029",
+   "metadata": {
+    "id": "2bc82609-a9fb-447a-a2af-99597c864029"
+   },
+   "source": [
+    "The Whisper model outputs a sequence of _token ids_. The tokenizer maps each of these token ids to their corresponding text string. For Hindi, we can load the pre-trained tokenizer and use it for fine-tuning without any further modifications. We simply have to \n",
+    "specify the target language and the task. These arguments inform the \n",
+    "tokenizer to prefix the language and task tokens to the start of encoded \n",
+    "label sequences:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c7b07f9b-ae0e-4f89-98f0-0c50d432eab6",
+   "metadata": {
+    "id": "c7b07f9b-ae0e-4f89-98f0-0c50d432eab6",
+    "outputId": "5c004b44-86e7-4e00-88be-39e0af5eed69"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from transformers import WhisperTokenizer\n",
+    "\n",
+    "tokenizer = WhisperTokenizer.from_pretrained(\"openai/whisper-small\", language=\"Hindi\", task=\"transcribe\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2ef23f3-f4a8-483a-a2dc-080a7496cb1b",
+   "metadata": {
+    "id": "d2ef23f3-f4a8-483a-a2dc-080a7496cb1b"
+   },
+   "source": [
+    "### Combine To Create A WhisperProcessor"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ff67654-5a29-4bb8-a69d-0228946c6f8d",
+   "metadata": {
+    "id": "5ff67654-5a29-4bb8-a69d-0228946c6f8d"
+   },
+   "source": [
+    "To simplify using the feature extractor and tokenizer, we can _wrap_ \n",
+    "both into a single `WhisperProcessor` class. This processor object \n",
+    "inherits from the `WhisperFeatureExtractor` and `WhisperProcessor`, \n",
+    "and can be used on the audio inputs and model predictions as required. \n",
+    "In doing so, we only need to keep track of two objects during training: \n",
+    "the `processor` and the `model`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "77d9f0c5-8607-4642-a8ac-c3ab2e223ea6",
+   "metadata": {
+    "id": "77d9f0c5-8607-4642-a8ac-c3ab2e223ea6"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from transformers import WhisperProcessor\n",
+    "\n",
+    "processor = WhisperProcessor.from_pretrained(\"openai/whisper-small\", language=\"Hindi\", task=\"transcribe\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "381acd09-0b0f-4d04-9eb3-f028ac0e5f2c",
+   "metadata": {
+    "id": "381acd09-0b0f-4d04-9eb3-f028ac0e5f2c"
+   },
+   "source": [
+    "### Prepare Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9649bf01-2e8a-45e5-8fca-441c13637b8f",
+   "metadata": {
+    "id": "9649bf01-2e8a-45e5-8fca-441c13637b8f"
+   },
+   "source": [
+    "Let's print the first example of the Common Voice dataset to see \n",
+    "what form the data is in:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6e6b0ec5-0c94-4e2c-ae24-c791be1b2255",
+   "metadata": {
+    "id": "6e6b0ec5-0c94-4e2c-ae24-c791be1b2255"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'audio': {'path': '/home/haroon/.cache/huggingface/datasets/downloads/extracted/19da7992f84c9f6fbb0b9f00f7d850f460c81cf35b4cf1f0c78fee7c0a9ceec8/hi_train_0/common_voice_hi_26008353.mp3', 'array': array([ 5.81611368e-26, -1.48634016e-25, -9.37040538e-26, ...,\n",
+      "        1.06425901e-07,  4.46416450e-08,  2.61450239e-09]), 'sampling_rate': 48000}, 'sentence': 'हमने उसका जन्मदिन मनाया।'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(common_voice[\"train\"][0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a679f05-063d-41b3-9b58-4fc9c6ccf4fd",
+   "metadata": {
+    "id": "5a679f05-063d-41b3-9b58-4fc9c6ccf4fd"
+   },
+   "source": [
+    "Since \n",
+    "our input audio is sampled at 48kHz, we need to _downsample_ it to \n",
+    "16kHz prior to passing it to the Whisper feature extractor, 16kHz being the sampling rate expected by the Whisper model. \n",
+    "\n",
+    "We'll set the audio inputs to the correct sampling rate using dataset's \n",
+    "[`cast_column`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=cast_column#datasets.DatasetDict.cast_column)\n",
+    "method. This operation does not change the audio in-place, \n",
+    "but rather signals to `datasets` to resample audio samples _on the fly_ the \n",
+    "first time that they are loaded:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "f12e2e57-156f-417b-8cfb-69221cc198e8",
+   "metadata": {
+    "id": "f12e2e57-156f-417b-8cfb-69221cc198e8"
+   },
+   "outputs": [],
+   "source": [
+    "from datasets import Audio\n",
+    "\n",
+    "common_voice = common_voice.cast_column(\"audio\", Audio(sampling_rate=16000))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00382a3e-abec-4cdd-a54c-d1aaa3ea4707",
+   "metadata": {
+    "id": "00382a3e-abec-4cdd-a54c-d1aaa3ea4707"
+   },
+   "source": [
+    "Re-loading the first audio sample in the Common Voice dataset will resample \n",
+    "it to the desired sampling rate:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "87122d71-289a-466a-afcf-fa354b18946b",
+   "metadata": {
+    "id": "87122d71-289a-466a-afcf-fa354b18946b"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'audio': {'path': '/home/haroon/.cache/huggingface/datasets/downloads/extracted/19da7992f84c9f6fbb0b9f00f7d850f460c81cf35b4cf1f0c78fee7c0a9ceec8/hi_train_0/common_voice_hi_26008353.mp3', 'array': array([ 3.81639165e-17,  2.42861287e-17, -1.73472348e-17, ...,\n",
+      "       -1.30981789e-07,  2.63096808e-07,  4.77157300e-08]), 'sampling_rate': 16000}, 'sentence': 'हमने उसका जन्मदिन मनाया।'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(common_voice[\"train\"][0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3df7378a-a4c0-45d7-8d07-defbd1062ab6",
+   "metadata": {},
+   "source": [
+    "We'll define our pre-processing strategy. We advise that you **do not** lower-case the transcriptions or remove punctuation unless mixing different datasets. This will enable you to fine-tune Whisper models that can predict punctuation and casing. Later, you will see how we can evaluate the predictions without punctuation or casing, so that the models benefit from the WER improvement obtained by normalising the transcriptions while still predicting fully formatted transcriptions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d041650e-1c48-4439-87b3-5b6f4a514107",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers.models.whisper.english_normalizer import BasicTextNormalizer\n",
+    "\n",
+    "do_lower_case = False\n",
+    "do_remove_punctuation = False\n",
+    "\n",
+    "normalizer = BasicTextNormalizer()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89e12c2e-2f14-479b-987b-f0c75c881095",
+   "metadata": {},
+   "source": [
+    "Now we can write a function to prepare our data ready for the model:\n",
+    "1. We load and resample the audio data by calling `batch[\"audio\"]`. As explained above, 🤗 Datasets performs any necessary resampling operations on the fly.\n",
+    "2. We use the feature extractor to compute the log-Mel spectrogram input features from our 1-dimensional audio array.\n",
+    "3. We perform any optional pre-processing (lower-case or remove punctuation).\n",
+    "4. We encode the transcriptions to label ids through the use of the tokenizer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "c085911c-a10a-41ef-8874-306e0503e9bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def prepare_dataset(batch):\n",
+    "    # load and (possibly) resample audio data to 16kHz\n",
+    "    audio = batch[\"audio\"]\n",
+    "\n",
+    "    # compute log-Mel input features from input audio array \n",
+    "    batch[\"input_features\"] = processor.feature_extractor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_features[0]\n",
+    "    # compute input length of audio sample in seconds\n",
+    "    batch[\"input_length\"] = len(audio[\"array\"]) / audio[\"sampling_rate\"]\n",
+    "    \n",
+    "    # optional pre-processing steps\n",
+    "    transcription = batch[\"sentence\"]\n",
+    "    if do_lower_case:\n",
+    "        transcription = transcription.lower()\n",
+    "    if do_remove_punctuation:\n",
+    "        transcription = normalizer(transcription).strip()\n",
+    "    \n",
+    "    # encode target text to label ids\n",
+    "    batch[\"labels\"] = processor.tokenizer(transcription).input_ids\n",
+    "    return batch"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c960965-9fb6-466f-9dbd-c9d43e71d9d0",
+   "metadata": {
+    "id": "70b319fb-2439-4ef6-a70d-a47bf41c4a13"
+   },
+   "source": [
+    "We can apply the data preparation function to all of our training examples using dataset's `.map` method. The argument `num_proc` specifies how many CPU cores to use. Setting `num_proc` > 1 will enable multiprocessing. If the `.map` method hangs with multiprocessing, set `num_proc=1` and process the dataset sequentially."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "7b73ab39-ffaf-4b9e-86e5-782963c6134b",
+   "metadata": {
+    "id": "7b73ab39-ffaf-4b9e-86e5-782963c6134b"
+   },
+   "outputs": [],
+   "source": [
+    "common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice.column_names[\"train\"], num_proc=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54ce0fdb-7218-4a4d-b175-383980fec0df",
+   "metadata": {},
+   "source": [
+    "Finally, we filter any training data with audio samples longer than 30s. These samples would otherwise be truncated by the Whisper feature-extractor which could affect the stability of training. We define a function that returns `True` for samples that are less than 30s, and `False` for those that are longer:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "01cb25ef-4bb0-4325-9461-f59198acadf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "max_input_length = 30.0\n",
+    "\n",
+    "def is_audio_in_length_range(length):\n",
+    "    return length < max_input_length"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30e676a8-7ca8-4850-8c5d-5b2b00d13fba",
+   "metadata": {},
+   "source": [
+    "We apply our filter function to all samples of our training dataset through 🤗 Datasets' `.filter` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "333f7f6e-6053-4d3b-8924-c733c79b82ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "common_voice[\"train\"] = common_voice[\"train\"].filter(\n",
+    "    is_audio_in_length_range,\n",
+    "    input_columns=[\"input_length\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "263a5a58-0239-4a25-b0df-c625fc9c5810",
+   "metadata": {
+    "id": "263a5a58-0239-4a25-b0df-c625fc9c5810"
+   },
+   "source": [
+    "## Training and Evaluation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a693e768-c5a6-453f-89a1-b601dcf7daf7",
+   "metadata": {
+    "id": "a693e768-c5a6-453f-89a1-b601dcf7daf7"
+   },
+   "source": [
+    "Now that we've prepared our data, we're ready to dive into the training pipeline. \n",
+    "The [🤗 Trainer](https://huggingface.co/transformers/master/main_classes/trainer.html?highlight=trainer)\n",
+    "will do much of the heavy lifting for us. All we have to do is:\n",
+    "\n",
+    "- Define a data collator: the data collator takes our pre-processed data and prepares PyTorch tensors ready for the model.\n",
+    "\n",
+    "- Evaluation metrics: during evaluation, we want to evaluate the model using the [word error rate (WER)](https://huggingface.co/metrics/wer) metric. We need to define a `compute_metrics` function that handles this computation.\n",
+    "\n",
+    "- Load a pre-trained checkpoint: we need to load a pre-trained checkpoint and configure it correctly for training.\n",
+    "\n",
+    "- Define the training configuration: this will be used by the 🤗 Trainer to define the training schedule.\n",
+    "\n",
+    "Once we've fine-tuned the model, we will evaluate it on the test data to verify that we have correctly trained it \n",
+    "to transcribe speech in Hindi."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d230e6d-624c-400a-bbf5-fa660881df25",
+   "metadata": {
+    "id": "8d230e6d-624c-400a-bbf5-fa660881df25"
+   },
+   "source": [
+    "### Define a Data Collator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04def221-0637-4a69-b242-d3f0c1d0ee78",
+   "metadata": {
+    "id": "04def221-0637-4a69-b242-d3f0c1d0ee78"
+   },
+   "source": [
+    "The data collator for a sequence-to-sequence speech model is unique in the sense that it \n",
+    "treats the `input_features` and `labels` independently: the  `input_features` must be \n",
+    "handled by the feature extractor and the `labels` by the tokenizer.\n",
+    "\n",
+    "The `input_features` are already padded to 30s and converted to a log-Mel spectrogram \n",
+    "of fixed dimension by action of the feature extractor, so all we have to do is convert the `input_features`\n",
+    "to batched PyTorch tensors. We do this using the feature extractor's `.pad` method with `return_tensors=pt`.\n",
+    "\n",
+    "The `labels` on the other hand are un-padded. We first pad the sequences\n",
+    "to the maximum length in the batch using the tokenizer's `.pad` method. The padding tokens \n",
+    "are then replaced by `-100` so that these tokens are **not** taken into account when \n",
+    "computing the loss. We then cut the BOS token from the start of the label sequence as we \n",
+    "append it later during training.\n",
+    "\n",
+    "We can leverage the `WhisperProcessor` we defined earlier to perform both the \n",
+    "feature extractor and the tokenizer operations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "8326221e-ec13-4731-bb4e-51e5fc1486c5",
+   "metadata": {
+    "id": "8326221e-ec13-4731-bb4e-51e5fc1486c5"
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "\n",
+    "from dataclasses import dataclass\n",
+    "from typing import Any, Dict, List, Union\n",
+    "\n",
+    "@dataclass\n",
+    "class DataCollatorSpeechSeq2SeqWithPadding:\n",
+    "    processor: Any\n",
+    "\n",
+    "    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:\n",
+    "        # split inputs and labels since they have to be of different lengths and need different padding methods\n",
+    "        # first treat the audio inputs by simply returning torch tensors\n",
+    "        input_features = [{\"input_features\": feature[\"input_features\"]} for feature in features]\n",
+    "        batch = self.processor.feature_extractor.pad(input_features, return_tensors=\"pt\")\n",
+    "\n",
+    "        # get the tokenized label sequences\n",
+    "        label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n",
+    "        # pad the labels to max length\n",
+    "        labels_batch = self.processor.tokenizer.pad(label_features, return_tensors=\"pt\")\n",
+    "\n",
+    "        # replace padding with -100 to ignore loss correctly\n",
+    "        labels = labels_batch[\"input_ids\"].masked_fill(labels_batch.attention_mask.ne(1), -100)\n",
+    "\n",
+    "        # if bos token is appended in previous tokenization step,\n",
+    "        # cut bos token here as it's append later anyways\n",
+    "        if (labels[:, 0] == self.processor.tokenizer.bos_token_id).all().cpu().item():\n",
+    "            labels = labels[:, 1:]\n",
+    "\n",
+    "        batch[\"labels\"] = labels\n",
+    "\n",
+    "        return batch"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3cae7dbf-8a50-456e-a3a8-7fd005390f86",
+   "metadata": {
+    "id": "3cae7dbf-8a50-456e-a3a8-7fd005390f86"
+   },
+   "source": [
+    "Let's initialise the data collator we've just defined:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "fc834702-c0d3-4a96-b101-7b87be32bf42",
+   "metadata": {
+    "id": "fc834702-c0d3-4a96-b101-7b87be32bf42"
+   },
+   "outputs": [],
+   "source": [
+    "data_collator = DataCollatorSpeechSeq2SeqWithPadding(processor=processor)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d62bb2ab-750a-45e7-82e9-61d6f4805698",
+   "metadata": {
+    "id": "d62bb2ab-750a-45e7-82e9-61d6f4805698"
+   },
+   "source": [
+    "### Evaluation Metrics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "66fee1a7-a44c-461e-b047-c3917221572e",
+   "metadata": {
+    "id": "66fee1a7-a44c-461e-b047-c3917221572e"
+   },
+   "source": [
+    "We'll use the word error rate (WER) metric, the 'de-facto' metric for assessing \n",
+    "ASR systems. For more information, refer to the WER [docs](https://huggingface.co/metrics/wer). We'll load the WER metric from 🤗 Evaluate:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "b22b4011-f31f-4b57-b684-c52332f92890",
+   "metadata": {
+    "id": "b22b4011-f31f-4b57-b684-c52332f92890"
+   },
+   "outputs": [],
+   "source": [
+    "import evaluate\n",
+    "\n",
+    "metric = evaluate.load(\"wer\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f32cab6-31f0-4cb9-af4c-40ba0f5fc508",
+   "metadata": {
+    "id": "4f32cab6-31f0-4cb9-af4c-40ba0f5fc508"
+   },
+   "source": [
+    "We then simply have to define a function that takes our model \n",
+    "predictions and returns the WER metric. This function, called\n",
+    "`compute_metrics`, first replaces `-100` with the `pad_token_id`\n",
+    "in the `label_ids` (undoing the step we applied in the \n",
+    "data collator to ignore padded tokens correctly in the loss).\n",
+    "It then decodes the predicted and label ids to strings. Finally,\n",
+    "it computes the WER between the predictions and reference labels. \n",
+    "Here, we have the option of evaluating with the 'normalised' transcriptions \n",
+    "and predictions. We recommend you set this to `True` to benefit from the WER \n",
+    "improvement obtained by normalising the transcriptions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "23959a70-22d0-4ffe-9fa1-72b61e75bb52",
+   "metadata": {
+    "id": "23959a70-22d0-4ffe-9fa1-72b61e75bb52"
+   },
+   "outputs": [],
+   "source": [
+    "# evaluate with the 'normalised' WER\n",
+    "do_normalize_eval = True\n",
+    "\n",
+    "def compute_metrics(pred):\n",
+    "    pred_ids = pred.predictions\n",
+    "    label_ids = pred.label_ids\n",
+    "\n",
+    "    # replace -100 with the pad_token_id\n",
+    "    label_ids[label_ids == -100] = processor.tokenizer.pad_token_id\n",
+    "\n",
+    "    # we do not want to group tokens when computing the metrics\n",
+    "    pred_str = processor.tokenizer.batch_decode(pred_ids, skip_special_tokens=True)\n",
+    "    label_str = processor.tokenizer.batch_decode(label_ids, skip_special_tokens=True)\n",
+    "\n",
+    "    if do_normalize_eval:\n",
+    "        pred_str = [normalizer(pred) for pred in pred_str]\n",
+    "        label_str = [normalizer(label) for label in label_str]\n",
+    "\n",
+    "    wer = 100 * metric.compute(predictions=pred_str, references=label_str)\n",
+    "\n",
+    "    return {\"wer\": wer}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "daf2a825-6d9f-4a23-b145-c37c0039075b",
+   "metadata": {
+    "id": "daf2a825-6d9f-4a23-b145-c37c0039075b"
+   },
+   "source": [
+    "### Load a Pre-Trained Checkpoint"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "437a97fa-4864-476b-8abc-f28b8166cfa5",
+   "metadata": {
+    "id": "437a97fa-4864-476b-8abc-f28b8166cfa5"
+   },
+   "source": [
+    "Now let's load the pre-trained Whisper `small` checkpoint. Again, this \n",
+    "is trivial through use of 🤗 Transformers!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "5a10cc4b-07ec-4ebd-ac1d-7c601023594f",
+   "metadata": {
+    "id": "5a10cc4b-07ec-4ebd-ac1d-7c601023594f"
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import WhisperForConditionalGeneration\n",
+    "\n",
+    "model = WhisperForConditionalGeneration.from_pretrained(\"openai/whisper-small\")\n",
+    "model.generation_config.language = \"hi\"  # define your language of choice here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a15ead5f-2277-4a39-937b-585c2497b2df",
+   "metadata": {
+    "id": "a15ead5f-2277-4a39-937b-585c2497b2df"
+   },
+   "source": [
+    "Override generation arguments - no tokens are forced as decoder outputs (see [`forced_decoder_ids`](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate.forced_decoder_ids)), no tokens are suppressed during generation (see [`suppress_tokens`](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate.suppress_tokens)). Set `use_cache` to False since we're using gradient checkpointing, and the two are incompatible:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "62038ba3-88ed-4fce-84db-338f50dcd04f",
+   "metadata": {
+    "id": "62038ba3-88ed-4fce-84db-338f50dcd04f"
+   },
+   "outputs": [],
+   "source": [
+    "model.config.forced_decoder_ids = None\n",
+    "model.config.suppress_tokens = []\n",
+    "model.config.use_cache = False"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2178dea4-80ca-47b6-b6ea-ba1915c90c06",
+   "metadata": {
+    "id": "2178dea4-80ca-47b6-b6ea-ba1915c90c06"
+   },
+   "source": [
+    "### Define the Training Configuration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c21af1e9-0188-4134-ac82-defc7bdcc436",
+   "metadata": {
+    "id": "c21af1e9-0188-4134-ac82-defc7bdcc436"
+   },
+   "source": [
+    "In the final step, we define all the parameters related to training. For more detail on the training arguments, refer to the Seq2SeqTrainingArguments [docs](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.Seq2SeqTrainingArguments)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "0ae3e9af-97b7-4aa0-ae85-20b23b5bcb3a",
+   "metadata": {
+    "id": "0ae3e9af-97b7-4aa0-ae85-20b23b5bcb3a"
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import Seq2SeqTrainingArguments\n",
+    "\n",
+    "training_args = Seq2SeqTrainingArguments(\n",
+    "    output_dir=\"./\",\n",
+    "    per_device_train_batch_size=8,\n",
+    "    gradient_accumulation_steps=8,  # increase by 2x for every 2x decrease in batch size\n",
+    "    learning_rate=1e-5,\n",
+    "    warmup_steps=500,\n",
+    "    max_steps=5000,\n",
+    "    gradient_checkpointing=True,\n",
+    "    fp16=True,\n",
+    "    evaluation_strategy=\"steps\",\n",
+    "    per_device_eval_batch_size=4,\n",
+    "    predict_with_generate=True,\n",
+    "    generation_max_length=225,\n",
+    "    save_steps=1000,\n",
+    "    eval_steps=1000,\n",
+    "    logging_steps=25,\n",
+    "    report_to=[\"tensorboard\"],\n",
+    "    load_best_model_at_end=True,\n",
+    "    metric_for_best_model=\"wer\",\n",
+    "    greater_is_better=False,\n",
+    "    push_to_hub=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3a944d8-3112-4552-82a0-be25988b3857",
+   "metadata": {
+    "id": "b3a944d8-3112-4552-82a0-be25988b3857"
+   },
+   "source": [
+    "**Note**: if one does not want to upload the model checkpoints to the Hub, \n",
+    "set `push_to_hub=False`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bac29114-d226-4f54-97cf-8718c9f94e1e",
+   "metadata": {
+    "id": "bac29114-d226-4f54-97cf-8718c9f94e1e"
+   },
+   "source": [
+    "We can forward the training arguments to the 🤗 Trainer along with our model,\n",
+    "dataset, data collator and `compute_metrics` function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "d546d7fe-0543-479a-b708-2ebabec19493",
+   "metadata": {
+    "id": "d546d7fe-0543-479a-b708-2ebabec19493"
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import Seq2SeqTrainer\n",
+    "\n",
+    "trainer = Seq2SeqTrainer(\n",
+    "    args=training_args,\n",
+    "    model=model,\n",
+    "    train_dataset=common_voice[\"train\"],\n",
+    "    eval_dataset=common_voice[\"test\"],\n",
+    "    data_collator=data_collator,\n",
+    "    compute_metrics=compute_metrics,\n",
+    "    tokenizer=processor.feature_extractor,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "uOrRhDGtN5S4",
+   "metadata": {
+    "id": "uOrRhDGtN5S4"
+   },
+   "source": [
+    "We'll save the processor object once before starting training. Since the processor is not trainable, it won't change over the course of training:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "-2zQwMfEOBJq",
+   "metadata": {
+    "id": "-2zQwMfEOBJq"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "processor.save_pretrained(training_args.output_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f404cf9-4345-468c-8196-4bd101d9bd51",
+   "metadata": {
+    "id": "7f404cf9-4345-468c-8196-4bd101d9bd51"
+   },
+   "source": [
+    "### Training"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e8b8d56-5a70-4f68-bd2e-f0752d0bd112",
+   "metadata": {
+    "id": "5e8b8d56-5a70-4f68-bd2e-f0752d0bd112"
+   },
+   "source": [
+    "Training will take approximately 5-10 hours depending on your GPU. The peak GPU memory for the given training configuration is approximately 36GB. \n",
+    "Depending on your GPU, it is possible that you will encounter a CUDA `\"out-of-memory\"` error when you launch training. \n",
+    "In this case, you can reduce the `per_device_train_batch_size` incrementally by factors of 2 \n",
+    "and employ [`gradient_accumulation_steps`](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.Seq2SeqTrainingArguments.gradient_accumulation_steps)\n",
+    "to compensate.\n",
+    "\n",
+    "To launch training, simply execute:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee8b7b8e-1c9a-4d77-9137-1778a629e6de",
+   "metadata": {
+    "id": "ee8b7b8e-1c9a-4d77-9137-1778a629e6de"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      \n",
+       "      <progress value='986' max='5000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      [ 986/5000 3:06:04 < 12:39:04, 0.09 it/s, Epoch 9.63/50]\n",
+       "    </div>\n",
+       "    <table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       " <tr style=\"text-align: left;\">\n",
+       "      <th>Step</th>\n",
+       "      <th>Training Loss</th>\n",
+       "      <th>Validation Loss</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table><p>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n",
+      "/home/haroon/python_virtual_envs/whisper_fine_tuning/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "trainer.train()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "810ced54-7187-4a06-b2fe-ba6dcca94dc3",
+   "metadata": {
+    "id": "810ced54-7187-4a06-b2fe-ba6dcca94dc3"
+   },
+   "source": [
+    "We can label our checkpoint with the `whisper-event` tag on push by setting the appropriate key-word arguments (kwargs):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c704f91e-241b-48c9-b8e0-f0da396a9663",
+   "metadata": {
+    "id": "c704f91e-241b-48c9-b8e0-f0da396a9663"
+   },
+   "outputs": [],
+   "source": [
+    "kwargs = {\n",
+    "    \"dataset_tags\": \"mozilla-foundation/common_voice_11_0\",\n",
+    "    \"dataset\": \"Common Voice 11.0\",  # a 'pretty' name for the training dataset\n",
+    "    \"language\": \"hi\",\n",
+    "    \"model_name\": \"Whisper Small Hi - Sanchit Gandhi\",  # a 'pretty' name for your model\n",
+    "    \"finetuned_from\": \"openai/whisper-small\",\n",
+    "    \"tasks\": \"automatic-speech-recognition\",\n",
+    "    \"tags\": \"whisper-event\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "090d676a-f944-4297-a938-a40eda0b2b68",
+   "metadata": {
+    "id": "090d676a-f944-4297-a938-a40eda0b2b68"
+   },
+   "source": [
+    "The training results can now be uploaded to the Hub. To do so, execute the `push_to_hub` command and save the preprocessor object we created:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d7030622-caf7-4039-939b-6195cdaa2585",
+   "metadata": {
+    "id": "d7030622-caf7-4039-939b-6195cdaa2585"
+   },
+   "outputs": [],
+   "source": [
+    "trainer.push_to_hub(**kwargs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca743fbd-602c-48d4-ba8d-a2fe60af64ba",
+   "metadata": {
+    "id": "ca743fbd-602c-48d4-ba8d-a2fe60af64ba"
+   },
+   "source": [
+    "## Closing Remarks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f737783-2870-4e35-aa11-86a42d7d997a",
+   "metadata": {
+    "id": "7f737783-2870-4e35-aa11-86a42d7d997a"
+   },
+   "source": [
+    "In this blog, we covered a step-by-step guide on fine-tuning Whisper for multilingual ASR \n",
+    "using 🤗 Datasets, Transformers and the Hugging Face Hub. For more details on the Whisper model, the Common Voice dataset and the theory behind fine-tuning, refere to the accompanying [blog post](https://huggingface.co/blog/fine-tune-whisper). If you're interested in fine-tuning other \n",
+    "Transformers models, both for English and multilingual ASR, be sure to check out the \n",
+    "examples scripts at [examples/pytorch/speech-recognition](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition)."
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "include_colab_link": true,
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/whisper-fine-tuning-event/fine-tune-whisper-non-streaming.py b/whisper-fine-tuning-event/fine-tune-whisper-non-streaming.py
new file mode 100644
index 0000000000000000000000000000000000000000..22f4860458b544e5982763f41280a52fd7034432
--- /dev/null
+++ b/whisper-fine-tuning-event/fine-tune-whisper-non-streaming.py
@@ -0,0 +1,554 @@
+#!/home/haroon/python_virtual_envs/whisper_fine_tuning/bin/python
+#!/usr/bin/env python
+# coding: utf-8
+
+# # Fine-Tune Whisper For Multilingual ASR with 🤗 Transformers
+
+# In this Colab, we present a step-by-step guide on how to fine-tune Whisper 
+# for any multilingual ASR dataset using Hugging Face 🤗 Transformers. This is a 
+# more "hands-on" version of the accompanying [blog post](https://huggingface.co/blog/fine-tune-whisper). 
+# For a more in-depth explanation of Whisper, the Common Voice dataset and the theory behind fine-tuning, the reader is advised to refer to the blog post.
+
+# ## Introduction
+
+# Whisper is a pre-trained model for automatic speech recognition (ASR) 
+# published in [September 2022](https://openai.com/blog/whisper/) by the authors 
+# Alec Radford et al. from OpenAI. Unlike many of its predecessors, such as 
+# [Wav2Vec 2.0](https://arxiv.org/abs/2006.11477), which are pre-trained 
+# on un-labelled audio data, Whisper is pre-trained on a vast quantity of 
+# **labelled** audio-transcription data, 680,000 hours to be precise. 
+# This is an order of magnitude more data than the un-labelled audio data used 
+# to train Wav2Vec 2.0 (60,000 hours). What is more, 117,000 hours of this 
+# pre-training data is multilingual ASR data. This results in checkpoints 
+# that can be applied to over 96 languages, many of which are considered 
+# _low-resource_.
+# 
+# When scaled to 680,000 hours of labelled pre-training data, Whisper models 
+# demonstrate a strong ability to generalise to many datasets and domains.
+# The pre-trained checkpoints achieve competitive results to state-of-the-art 
+# ASR systems, with near 3% word error rate (WER) on the test-clean subset of 
+# LibriSpeech ASR and a new state-of-the-art on TED-LIUM with 4.7% WER (_c.f._ 
+# Table 8 of the [Whisper paper](https://cdn.openai.com/papers/whisper.pdf)).
+# The extensive multilingual ASR knowledge acquired by Whisper during pre-training 
+# can be leveraged for other low-resource languages; through fine-tuning, the 
+# pre-trained checkpoints can be adapted for specific datasets and languages 
+# to further improve upon these results. We'll show just how Whisper can be fine-tuned 
+# for low-resource languages in this Colab.
+
+# <figure>
+# <img src="https://raw.githubusercontent.com/sanchit-gandhi/notebooks/main/whisper_architecture.svg" alt="Trulli" style="width:100%">
+# <figcaption align = "center"><b>Figure 1:</b> Whisper model. The architecture 
+# follows the standard Transformer-based encoder-decoder model. A 
+# log-Mel spectrogram is input to the encoder. The last encoder 
+# hidden states are input to the decoder via cross-attention mechanisms. The 
+# decoder autoregressively predicts text tokens, jointly conditional on the 
+# encoder hidden states and previously predicted tokens. Figure source: 
+# <a href="https://openai.com/blog/whisper/">OpenAI Whisper Blog</a>.</figcaption>
+# </figure>
+
+# The Whisper checkpoints come in five configurations of varying model sizes.
+# The smallest four are trained on either English-only or multilingual data.
+# The largest checkpoint is multilingual only. All nine of the pre-trained checkpoints 
+# are available on the [Hugging Face Hub](https://huggingface.co/models?search=openai/whisper). The 
+# checkpoints are summarised in the following table with links to the models on the Hub:
+# 
+# | Size   | Layers | Width | Heads | Parameters | English-only                                         | Multilingual                                      |
+# |--------|--------|-------|-------|------------|------------------------------------------------------|---------------------------------------------------|
+# | tiny   | 4      | 384   | 6     | 39 M       | [✓](https://huggingface.co/openai/whisper-tiny.en)   | [✓](https://huggingface.co/openai/whisper-tiny.)  |
+# | base   | 6      | 512   | 8     | 74 M       | [✓](https://huggingface.co/openai/whisper-base.en)   | [✓](https://huggingface.co/openai/whisper-base)   |
+# | small  | 12     | 768   | 12    | 244 M      | [✓](https://huggingface.co/openai/whisper-small.en)  | [✓](https://huggingface.co/openai/whisper-small)  |
+# | medium | 24     | 1024  | 16    | 769 M      | [✓](https://huggingface.co/openai/whisper-medium.en) | [✓](https://huggingface.co/openai/whisper-medium) |
+# | large  | 32     | 1280  | 20    | 1550 M     | x                                                    | [✓](https://huggingface.co/openai/whisper-large)  |
+# 
+# For demonstration purposes, we'll fine-tune the multilingual version of the 
+# [`"small"`](https://huggingface.co/openai/whisper-small) checkpoint with 244M params (~= 1GB). 
+# As for our data, we'll train and evaluate our system on a low-resource language 
+# taken from the [Common Voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0)
+# dataset. We'll show that with as little as 8 hours of fine-tuning data, we can achieve 
+# strong performance in this language.
+
+# ------------------------------------------------------------------------
+# 
+# \\({}^1\\) The name Whisper follows from the acronym “WSPSR”, which stands for “Web-scale Supervised Pre-training for Speech Recognition”.
+
+# ## Load Dataset
+
+# Using 🤗 Datasets, downloading and preparing data is extremely simple. 
+# We can download and prepare the Common Voice splits in just one line of code. 
+# 
+# First, ensure you have accepted the terms of use on the Hugging Face Hub: [mozilla-foundation/common_voice_11_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0). Once you have accepted the terms, you will have full access to the dataset and be able to download the data locally.
+# 
+# Since Hindi is very low-resource, we'll combine the `train` and `validation` 
+# splits to give approximately 8 hours of training data. We'll use the 4 hours 
+# of `test` data as our held-out test set:
+
+# In[1]:
+
+
+from datasets import load_dataset, DatasetDict
+
+common_voice = DatasetDict()
+
+common_voice["train"] = load_dataset("mozilla-foundation/common_voice_11_0", "hi", split="train+validation", token=True)
+common_voice["test"] = load_dataset("mozilla-foundation/common_voice_11_0", "hi", split="test", token=True)
+
+print(common_voice)
+
+
+# Most ASR datasets only provide input audio samples (`audio`) and the 
+# corresponding transcribed text (`sentence`). Common Voice contains additional 
+# metadata information, such as `accent` and `locale`, which we can disregard for ASR.
+# Keeping the notebook as general as possible, we only consider the input audio and
+# transcribed text for fine-tuning, discarding the additional metadata information:
+
+# In[2]:
+
+
+common_voice = common_voice.remove_columns(["accent", "age", "client_id", "down_votes", "gender", "locale", "path", "segment", "up_votes"])
+
+print(common_voice)
+
+
+# ## Prepare Feature Extractor, Tokenizer and Data
+
+# The ASR pipeline can be de-composed into three stages: 
+# 1) A feature extractor which pre-processes the raw audio-inputs
+# 2) The model which performs the sequence-to-sequence mapping 
+# 3) A tokenizer which post-processes the model outputs to text format
+# 
+# In 🤗 Transformers, the Whisper model has an associated feature extractor and tokenizer, 
+# called [WhisperFeatureExtractor](https://huggingface.co/docs/transformers/main/model_doc/whisper#transformers.WhisperFeatureExtractor)
+# and [WhisperTokenizer](https://huggingface.co/docs/transformers/main/model_doc/whisper#transformers.WhisperTokenizer) 
+# respectively.
+# 
+# We'll go through details for setting-up the feature extractor and tokenizer one-by-one!
+
+# ### Load WhisperFeatureExtractor
+
+# The Whisper feature extractor performs two operations:
+# 1. Pads / truncates the audio inputs to 30s: any audio inputs shorter than 30s are padded to 30s with silence (zeros), and those longer that 30s are truncated to 30s
+# 2. Converts the audio inputs to _log-Mel spectrogram_ input features, a visual representation of the audio and the form of the input expected by the Whisper model
+
+# <figure>
+# <img src="https://raw.githubusercontent.com/sanchit-gandhi/notebooks/main/spectrogram.jpg" alt="Trulli" style="width:100%">
+# <figcaption align = "center"><b>Figure 2:</b> Conversion of sampled audio array to log-Mel spectrogram.
+# Left: sampled 1-dimensional audio signal. Right: corresponding log-Mel spectrogram. Figure source:
+# <a href="https://ai.googleblog.com/2019/04/specaugment-new-data-augmentation.html">Google SpecAugment Blog</a>.
+# </figcaption>
+
+# We'll load the feature extractor from the pre-trained checkpoint with the default values:
+
+# In[3]:
+
+
+from transformers import WhisperFeatureExtractor
+
+feature_extractor = WhisperFeatureExtractor.from_pretrained("openai/whisper-small")
+
+
+# ### Load WhisperTokenizer
+
+# The Whisper model outputs a sequence of _token ids_. The tokenizer maps each of these token ids to their corresponding text string. For Hindi, we can load the pre-trained tokenizer and use it for fine-tuning without any further modifications. We simply have to 
+# specify the target language and the task. These arguments inform the 
+# tokenizer to prefix the language and task tokens to the start of encoded 
+# label sequences:
+
+# In[4]:
+
+
+from transformers import WhisperTokenizer
+
+tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-small", language="Hindi", task="transcribe")
+
+
+# ### Combine To Create A WhisperProcessor
+
+# To simplify using the feature extractor and tokenizer, we can _wrap_ 
+# both into a single `WhisperProcessor` class. This processor object 
+# inherits from the `WhisperFeatureExtractor` and `WhisperProcessor`, 
+# and can be used on the audio inputs and model predictions as required. 
+# In doing so, we only need to keep track of two objects during training: 
+# the `processor` and the `model`:
+
+# In[5]:
+
+
+from transformers import WhisperProcessor
+
+processor = WhisperProcessor.from_pretrained("openai/whisper-small", language="Hindi", task="transcribe")
+
+
+# ### Prepare Data
+
+# Let's print the first example of the Common Voice dataset to see 
+# what form the data is in:
+
+# In[6]:
+
+
+print(common_voice["train"][0])
+
+
+# Since 
+# our input audio is sampled at 48kHz, we need to _downsample_ it to 
+# 16kHz prior to passing it to the Whisper feature extractor, 16kHz being the sampling rate expected by the Whisper model. 
+# 
+# We'll set the audio inputs to the correct sampling rate using dataset's 
+# [`cast_column`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=cast_column#datasets.DatasetDict.cast_column)
+# method. This operation does not change the audio in-place, 
+# but rather signals to `datasets` to resample audio samples _on the fly_ the 
+# first time that they are loaded:
+
+# In[7]:
+
+
+from datasets import Audio
+
+common_voice = common_voice.cast_column("audio", Audio(sampling_rate=16000))
+
+
+# Re-loading the first audio sample in the Common Voice dataset will resample 
+# it to the desired sampling rate:
+
+# In[8]:
+
+
+print(common_voice["train"][0])
+
+
+# We'll define our pre-processing strategy. We advise that you **do not** lower-case the transcriptions or remove punctuation unless mixing different datasets. This will enable you to fine-tune Whisper models that can predict punctuation and casing. Later, you will see how we can evaluate the predictions without punctuation or casing, so that the models benefit from the WER improvement obtained by normalising the transcriptions while still predicting fully formatted transcriptions.
+
+# In[9]:
+
+
+from transformers.models.whisper.english_normalizer import BasicTextNormalizer
+
+do_lower_case = False
+do_remove_punctuation = False
+
+normalizer = BasicTextNormalizer()
+
+
+# Now we can write a function to prepare our data ready for the model:
+# 1. We load and resample the audio data by calling `batch["audio"]`. As explained above, 🤗 Datasets performs any necessary resampling operations on the fly.
+# 2. We use the feature extractor to compute the log-Mel spectrogram input features from our 1-dimensional audio array.
+# 3. We perform any optional pre-processing (lower-case or remove punctuation).
+# 4. We encode the transcriptions to label ids through the use of the tokenizer.
+
+# In[10]:
+
+
+def prepare_dataset(batch):
+    # load and (possibly) resample audio data to 16kHz
+    audio = batch["audio"]
+
+    # compute log-Mel input features from input audio array 
+    batch["input_features"] = processor.feature_extractor(audio["array"], sampling_rate=audio["sampling_rate"]).input_features[0]
+    # compute input length of audio sample in seconds
+    batch["input_length"] = len(audio["array"]) / audio["sampling_rate"]
+    
+    # optional pre-processing steps
+    transcription = batch["sentence"]
+    if do_lower_case:
+        transcription = transcription.lower()
+    if do_remove_punctuation:
+        transcription = normalizer(transcription).strip()
+    
+    # encode target text to label ids
+    batch["labels"] = processor.tokenizer(transcription).input_ids
+    return batch
+
+
+# We can apply the data preparation function to all of our training examples using dataset's `.map` method. The argument `num_proc` specifies how many CPU cores to use. Setting `num_proc` > 1 will enable multiprocessing. If the `.map` method hangs with multiprocessing, set `num_proc=1` and process the dataset sequentially.
+
+# In[11]:
+
+
+common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice.column_names["train"], num_proc=2)
+
+
+# Finally, we filter any training data with audio samples longer than 30s. These samples would otherwise be truncated by the Whisper feature-extractor which could affect the stability of training. We define a function that returns `True` for samples that are less than 30s, and `False` for those that are longer:
+
+# In[12]:
+
+
+max_input_length = 30.0
+
+def is_audio_in_length_range(length):
+    return length < max_input_length
+
+
+# We apply our filter function to all samples of our training dataset through 🤗 Datasets' `.filter` method:
+
+# In[13]:
+
+
+common_voice["train"] = common_voice["train"].filter(
+    is_audio_in_length_range,
+    input_columns=["input_length"],
+)
+
+
+# ## Training and Evaluation
+
+# Now that we've prepared our data, we're ready to dive into the training pipeline. 
+# The [🤗 Trainer](https://huggingface.co/transformers/master/main_classes/trainer.html?highlight=trainer)
+# will do much of the heavy lifting for us. All we have to do is:
+# 
+# - Define a data collator: the data collator takes our pre-processed data and prepares PyTorch tensors ready for the model.
+# 
+# - Evaluation metrics: during evaluation, we want to evaluate the model using the [word error rate (WER)](https://huggingface.co/metrics/wer) metric. We need to define a `compute_metrics` function that handles this computation.
+# 
+# - Load a pre-trained checkpoint: we need to load a pre-trained checkpoint and configure it correctly for training.
+# 
+# - Define the training configuration: this will be used by the 🤗 Trainer to define the training schedule.
+# 
+# Once we've fine-tuned the model, we will evaluate it on the test data to verify that we have correctly trained it 
+# to transcribe speech in Hindi.
+
+# ### Define a Data Collator
+
+# The data collator for a sequence-to-sequence speech model is unique in the sense that it 
+# treats the `input_features` and `labels` independently: the  `input_features` must be 
+# handled by the feature extractor and the `labels` by the tokenizer.
+# 
+# The `input_features` are already padded to 30s and converted to a log-Mel spectrogram 
+# of fixed dimension by action of the feature extractor, so all we have to do is convert the `input_features`
+# to batched PyTorch tensors. We do this using the feature extractor's `.pad` method with `return_tensors=pt`.
+# 
+# The `labels` on the other hand are un-padded. We first pad the sequences
+# to the maximum length in the batch using the tokenizer's `.pad` method. The padding tokens 
+# are then replaced by `-100` so that these tokens are **not** taken into account when 
+# computing the loss. We then cut the BOS token from the start of the label sequence as we 
+# append it later during training.
+# 
+# We can leverage the `WhisperProcessor` we defined earlier to perform both the 
+# feature extractor and the tokenizer operations:
+
+# In[14]:
+
+
+import torch
+
+from dataclasses import dataclass
+from typing import Any, Dict, List, Union
+
+@dataclass
+class DataCollatorSpeechSeq2SeqWithPadding:
+    processor: Any
+
+    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
+        # split inputs and labels since they have to be of different lengths and need different padding methods
+        # first treat the audio inputs by simply returning torch tensors
+        input_features = [{"input_features": feature["input_features"]} for feature in features]
+        batch = self.processor.feature_extractor.pad(input_features, return_tensors="pt")
+
+        # get the tokenized label sequences
+        label_features = [{"input_ids": feature["labels"]} for feature in features]
+        # pad the labels to max length
+        labels_batch = self.processor.tokenizer.pad(label_features, return_tensors="pt")
+
+        # replace padding with -100 to ignore loss correctly
+        labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)
+
+        # if bos token is appended in previous tokenization step,
+        # cut bos token here as it's append later anyways
+        if (labels[:, 0] == self.processor.tokenizer.bos_token_id).all().cpu().item():
+            labels = labels[:, 1:]
+
+        batch["labels"] = labels
+
+        return batch
+
+
+# Let's initialise the data collator we've just defined:
+
+# In[15]:
+
+
+data_collator = DataCollatorSpeechSeq2SeqWithPadding(processor=processor)
+
+
+# ### Evaluation Metrics
+
+# We'll use the word error rate (WER) metric, the 'de-facto' metric for assessing 
+# ASR systems. For more information, refer to the WER [docs](https://huggingface.co/metrics/wer). We'll load the WER metric from 🤗 Evaluate:
+
+# In[16]:
+
+
+import evaluate
+
+metric = evaluate.load("wer")
+
+
+# We then simply have to define a function that takes our model 
+# predictions and returns the WER metric. This function, called
+# `compute_metrics`, first replaces `-100` with the `pad_token_id`
+# in the `label_ids` (undoing the step we applied in the 
+# data collator to ignore padded tokens correctly in the loss).
+# It then decodes the predicted and label ids to strings. Finally,
+# it computes the WER between the predictions and reference labels. 
+# Here, we have the option of evaluating with the 'normalised' transcriptions 
+# and predictions. We recommend you set this to `True` to benefit from the WER 
+# improvement obtained by normalising the transcriptions.
+
+# In[17]:
+
+
+# evaluate with the 'normalised' WER
+do_normalize_eval = True
+
+def compute_metrics(pred):
+    pred_ids = pred.predictions
+    label_ids = pred.label_ids
+
+    # replace -100 with the pad_token_id
+    label_ids[label_ids == -100] = processor.tokenizer.pad_token_id
+
+    # we do not want to group tokens when computing the metrics
+    pred_str = processor.tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
+    label_str = processor.tokenizer.batch_decode(label_ids, skip_special_tokens=True)
+
+    if do_normalize_eval:
+        pred_str = [normalizer(pred) for pred in pred_str]
+        label_str = [normalizer(label) for label in label_str]
+
+    wer = 100 * metric.compute(predictions=pred_str, references=label_str)
+
+    return {"wer": wer}
+
+
+# ### Load a Pre-Trained Checkpoint
+
+# Now let's load the pre-trained Whisper `small` checkpoint. Again, this 
+# is trivial through use of 🤗 Transformers!
+
+# In[18]:
+
+
+from transformers import WhisperForConditionalGeneration
+
+model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")
+model.generation_config.language = "hi"  # define your language of choice here
+
+
+# Override generation arguments - no tokens are forced as decoder outputs (see [`forced_decoder_ids`](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate.forced_decoder_ids)), no tokens are suppressed during generation (see [`suppress_tokens`](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate.suppress_tokens)). Set `use_cache` to False since we're using gradient checkpointing, and the two are incompatible:
+
+# In[19]:
+
+
+model.config.forced_decoder_ids = None
+model.config.suppress_tokens = []
+model.config.use_cache = False
+
+
+# ### Define the Training Configuration
+
+# In the final step, we define all the parameters related to training. For more detail on the training arguments, refer to the Seq2SeqTrainingArguments [docs](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.Seq2SeqTrainingArguments).
+
+# In[20]:
+
+
+from transformers import Seq2SeqTrainingArguments
+
+training_args = Seq2SeqTrainingArguments(
+    output_dir="./",
+    per_device_train_batch_size=8,
+    gradient_accumulation_steps=8,  # increase by 2x for every 2x decrease in batch size
+    learning_rate=1e-5,
+    warmup_steps=500,
+    max_steps=5000,
+    gradient_checkpointing=True,
+    fp16=True,
+    evaluation_strategy="steps",
+    per_device_eval_batch_size=4,
+    predict_with_generate=True,
+    generation_max_length=225,
+    save_steps=1000,
+    eval_steps=1000,
+    logging_steps=25,
+    report_to=["tensorboard"],
+    load_best_model_at_end=True,
+    metric_for_best_model="wer",
+    greater_is_better=False,
+    push_to_hub=True,
+)
+
+
+# **Note**: if one does not want to upload the model checkpoints to the Hub, 
+# set `push_to_hub=False`.
+
+# We can forward the training arguments to the 🤗 Trainer along with our model,
+# dataset, data collator and `compute_metrics` function:
+
+# In[21]:
+
+
+from transformers import Seq2SeqTrainer
+
+trainer = Seq2SeqTrainer(
+    args=training_args,
+    model=model,
+    train_dataset=common_voice["train"],
+    eval_dataset=common_voice["test"],
+    data_collator=data_collator,
+    compute_metrics=compute_metrics,
+    tokenizer=processor.feature_extractor,
+)
+
+
+# We'll save the processor object once before starting training. Since the processor is not trainable, it won't change over the course of training:
+
+# In[22]:
+
+
+processor.save_pretrained(training_args.output_dir)
+
+
+# ### Training
+
+# Training will take approximately 5-10 hours depending on your GPU. The peak GPU memory for the given training configuration is approximately 36GB. 
+# Depending on your GPU, it is possible that you will encounter a CUDA `"out-of-memory"` error when you launch training. 
+# In this case, you can reduce the `per_device_train_batch_size` incrementally by factors of 2 
+# and employ [`gradient_accumulation_steps`](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.Seq2SeqTrainingArguments.gradient_accumulation_steps)
+# to compensate.
+# 
+# To launch training, simply execute:
+
+# In[ ]:
+
+
+trainer.train()
+
+
+# We can label our checkpoint with the `whisper-event` tag on push by setting the appropriate key-word arguments (kwargs):
+
+# In[ ]:
+
+
+kwargs = {
+    "dataset_tags": "mozilla-foundation/common_voice_11_0",
+    "dataset": "Common Voice 11.0",  # a 'pretty' name for the training dataset
+    "language": "hi",
+    "model_name": "Whisper Small Hi - Sanchit Gandhi",  # a 'pretty' name for your model
+    "finetuned_from": "openai/whisper-small",
+    "tasks": "automatic-speech-recognition",
+    "tags": "whisper-event",
+}
+
+
+# The training results can now be uploaded to the Hub. To do so, execute the `push_to_hub` command and save the preprocessor object we created:
+
+# In[ ]:
+
+
+trainer.push_to_hub(**kwargs)
+
+
+# ## Closing Remarks
+
+# In this blog, we covered a step-by-step guide on fine-tuning Whisper for multilingual ASR 
+# using 🤗 Datasets, Transformers and the Hugging Face Hub. For more details on the Whisper model, the Common Voice dataset and the theory behind fine-tuning, refere to the accompanying [blog post](https://huggingface.co/blog/fine-tune-whisper). If you're interested in fine-tuning other 
+# Transformers models, both for English and multilingual ASR, be sure to check out the 
+# examples scripts at [examples/pytorch/speech-recognition](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition).
diff --git a/whisper-fine-tuning-event/fine-tune-whisper-streaming.ipynb b/whisper-fine-tuning-event/fine-tune-whisper-streaming.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..4da8d74c2d1d2282654309ddba4663181a9660cd
--- /dev/null
+++ b/whisper-fine-tuning-event/fine-tune-whisper-streaming.ipynb
@@ -0,0 +1,883 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "75b58048-7d14-4fc6-8085-1fc08c81b4a6",
+   "metadata": {},
+   "source": [
+    "# Fine-Tune Whisper With 🤗 Transformers and Streaming Mode"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbfa8ad5-4cdc-4512-9058-836cbbf65e1a",
+   "metadata": {},
+   "source": [
+    "In this Colab, we present a step-by-step guide on fine-tuning Whisper with Hugging Face 🤗 Transformers on 400 hours of speech data! Using streaming mode, we'll show how you can train a speech recongition model on any dataset, irrespective of size. With streaming mode, storage requirements are no longer a consideration: you can train a model on whatever dataset you want, even if it's download size exceeds your devices disk space. How can this be possible? It simply seems too good to be true! Well, rest assured it's not 😉 Carry on reading to find out more."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "afe0d503-ae4e-4aa7-9af4-dbcba52db41e",
+   "metadata": {},
+   "source": [
+    "## Introduction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ae91ed4-9c3e-4ade-938e-f4c2dcfbfdc0",
+   "metadata": {},
+   "source": [
+    "Speech recognition datasets are large. A typical speech dataset consists of approximately 100 hours of audio-transcription data, requiring upwards of 130GB of storage space for download and preparation. For most ASR researchers, this is already at the upper limit of what is feasible for disk space. So what happens when we want to train on a larger dataset? The full [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) dataset consists of 960 hours of audio data. Kensho's [SPGISpeech](https://huggingface.co/datasets/kensho/spgispeech) contains 5,000 hours of audio data. ML Commons [People's Speech](https://huggingface.co/datasets/MLCommons/peoples_speech) contains **30,000+** hours of audio data! Do we need to bite the bullet and buy additional storage? Or is there a way we can train on all of these datasets with no disk drive requirements?\n",
+    "\n",
+    "When training machine learning systems, we rarely use the entire dataset at once. We typically _batch_ our data into smaller subsets of data, and pass these incrementally through our training pipeline. This is because we train our system on an accelerator device, such as a GPU or TPU, which has a memory limit typically around 16GB. We have to fit our model, optimiser and training data all on the same accelerator device, so we usually have to divide the dataset up into smaller batches and move them from the CPU to the GPU when required.\n",
+    "\n",
+    "Consequently, we don't require the entire dataset to be downloaded at once; we simply need the batch of data that we pass to our model at any one go. We can leverage this principle of partial dataset loading when preparing our dataset: rather than downloading the entire dataset at the start, we can load each piece of data as and when we need it. For each batch, we load the relevant data from a remote server and pass it through the training pipeline. For the next batch, we load the next items and again pass them through the training pipeline. At no point do we have to save data to our disk drive, we simply load them in memory and use them in our pipeline. In doing so, we only ever need as much memory as each individual batch requires.\n",
+    "\n",
+    "This is analogous to downloading a TV show versus streaming it 📺 When we download a TV show, we download the entire video offline and save it to our disk. Compare this to when we stream a TV show. Here, we don't download any part of the video to memory, but iterate over the video file and load each part in real-time as required. It's this same principle that we can apply to our ML training pipeline! We want to iterate over the dataset and load each sample of data as required.\n",
+    "\n",
+    "While the principle of partial dataset loading sounds ideal, it also seems **pretty** difficult to do. Luckily for us, 🤗 Datasets allows us to do this with minimal code changes! We'll make use of the principle of [_streaming_](https://huggingface.co/docs/datasets/stream), depicted graphically in Figure 1. Streaming does exactly this: the data is loaded progressively as we iterate over the dataset, meaning it is only loaded as and when we need it. If you're familiar with 🤗 Transformers and Datasets, the content of this notebook will be very familiar, with some small extensions to support streaming mode."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c87f76e-47be-4a5d-bc52-7b1c2e9d4f5a",
+   "metadata": {},
+   "source": [
+    "<figure>\n",
+    "<img src=\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/datasets/streaming.gif\" alt=\"Trulli\" style=\"width:100%\">\n",
+    "<figcaption align = \"center\"><b>Figure 1:</b> Streaming mode. The dataset is divided into smaller subsets, with subsets loaded progressively as we iterate over the dataset. </figcaption>\n",
+    "</figure>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21b6316e-8a55-4549-a154-66d3da2ab74a",
+   "metadata": {},
+   "source": [
+    "This notebook provides a guide to fine-tuning on the task of _speech recognition_, which involves learning a\n",
+    "mapping from speech to text. Speech recognition is divided into two categories: English-only or multilingual (all other languages). \n",
+    "This notebook applies to both categories, with pointers for changing between languages and datasets.\n",
+    "\n",
+    "As for our model, we'll fine-tune the Whisper model released in [September 2022](https://openai.com/blog/whisper/) by the authors \n",
+    "Alec Radford et al. from OpenAI. Whisper is an encoder-decoder model pre-trained on 680k hours of labelled audio-transcription data. \n",
+    "It achieves strong performance on many speech recognition and speech translation datasets without fine-tuning. With fine-tuning, \n",
+    "we aim to improve upon these results further, with many SoTA results up for grabs! For a full explanation on the Whisper model, the \n",
+    "reader is advised to read the blog post [Fine-Tune Whisper with 🤗 Transformers](https://huggingface.co/blog/fine-tune-whisper#introduction).\n",
+    "\n",
+    "The Whisper checkpoints come in five configurations of varying model sizes.\n",
+    "The smallest four are trained on either English-only or multilingual data.\n",
+    "The largest checkpoint is multilingual only. All nine of the pre-trained checkpoints \n",
+    "are available on the [Hugging Face Hub](https://huggingface.co/models?search=openai/whisper). The \n",
+    "checkpoints are summarised in the following table with links to the models on the Hub:\n",
+    "\n",
+    "| Size   | Layers | Width | Heads | Parameters | English-only                                         | Multilingual                                      |\n",
+    "|--------|--------|-------|-------|------------|------------------------------------------------------|---------------------------------------------------|\n",
+    "| tiny   | 4      | 384   | 6     | 39 M       | [✓](https://huggingface.co/openai/whisper-tiny.en)   | [✓](https://huggingface.co/openai/whisper-tiny.)  |\n",
+    "| base   | 6      | 512   | 8     | 74 M       | [✓](https://huggingface.co/openai/whisper-base.en)   | [✓](https://huggingface.co/openai/whisper-base)   |\n",
+    "| small  | 12     | 768   | 12    | 244 M      | [✓](https://huggingface.co/openai/whisper-small.en)  | [✓](https://huggingface.co/openai/whisper-small)  |\n",
+    "| medium | 24     | 1024  | 16    | 769 M      | [✓](https://huggingface.co/openai/whisper-medium.en) | [✓](https://huggingface.co/openai/whisper-medium) |\n",
+    "| large  | 32     | 1280  | 20    | 1550 M     | x                                                    | [✓](https://huggingface.co/openai/whisper-large)  |\n",
+    "\n",
+    "When fine-tuning on an English dataset for speech recognition, it is recommeneded to select one of the English-only checkpoints. For any other language, it is recommended to select a multilingual checkpoint.\n",
+    "\n",
+    "For demonstration purposes, we'll fine-tune the multilingual version of the \n",
+    "[`\"small\"`](https://huggingface.co/openai/whisper-small) checkpoint with 244M params (~= 1GB). \n",
+    "As for our data, we'll train and evaluate our system on 400 hours of multilingual speech recognition data\n",
+    "taken from the [Common Voice 11](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0)\n",
+    "dataset. We'll show how we can train a model on 400 hours of training data using the default disk space \n",
+    "that comes with a standard GPU device or Google Colab."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b219c9dd-39b6-4a95-b2a1-3f547a1e7bc0",
+   "metadata": {},
+   "source": [
+    "## Load Dataset with Streaming"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b17a4763-4381-4157-ae38-b04a8b5f1c43",
+   "metadata": {},
+   "source": [
+    "This is where the magic happens! We'll first write a wrapper function around 🤗 Datasets `load_dataset` method. This function downloads the required splits using streaming mode by forcing `streaming=True` in the `load_dataset` method. Multiple splits can be combined (interleaved) by concatenating them with the \"+\" symbol when specifying the split name, e.g. `split=train+validation` will return a single split with the training and validation splits interleaved together. The function has the same arguments and key-word arguments as 🤗 Datasets `load_dataset` method, so we can use it in exactly the same way!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "065a8cf7-e54f-4ac3-900e-609c80714fca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datasets import interleave_datasets, load_dataset\n",
+    "\n",
+    "def load_streaming_dataset(dataset_name, dataset_config_name, split, **kwargs):\n",
+    "    if \"+\" in split:\n",
+    "        # load multiple splits separated by the `+` symbol *with* streaming mode\n",
+    "        dataset_splits = [load_dataset(dataset_name, dataset_config_name, split=split_name, streaming=True, **kwargs) for split_name in split.split(\"+\")]\n",
+    "        # interleave multiple splits to form one dataset\n",
+    "        interleaved_dataset = interleave_datasets(dataset_splits)\n",
+    "        return interleaved_dataset\n",
+    "    else:\n",
+    "        # load a single split *with* streaming mode\n",
+    "        dataset = load_dataset(dataset_name, dataset_config_name, split=split, streaming=True, **kwargs)\n",
+    "        return dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "674429c5-0ab4-4adf-975b-621bb69eca38",
+   "metadata": {},
+   "source": [
+    "We'll train our system on the Spanish split of [Common Voice 11](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0). We can see how much training data we have by viewing the [language page](https://commonvoice.mozilla.org/en/datasets) on the Common Voice website. The Spanish split has over 400 hours of labelled training data - that's enourmous! More than we could ever fit on a Google Colab or a standard workstation. But with streaming mode, we'll only download data as and when we need it, making training on this dataset possible!\n",
+    "\n",
+    "Since Spanish is relatively high-resource, we'll only use the `train` split for training and the `test` split for evaluation. If you're training on a low-resource language, such as the Hindi split of Common Voice 11, it's worth combining the `train` and `validation` splits to give a larger training set. You can achieve this by setting: `split=\"train+validation\"` for the training split.\n",
+    "\n",
+    "If you're using a gated dataset, like Common Voice 11, ensure you have accepted the terms of use on the Hugging Face Hub: [mozilla-foundation/common_voice_11_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0). Once you have accepted the terms, you will have full access to the dataset and be able to load the data locally."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a2787582-554f-44ce-9f38-4180a5ed6b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datasets import IterableDatasetDict\n",
+    "\n",
+    "raw_datasets = IterableDatasetDict()\n",
+    "\n",
+    "raw_datasets[\"train\"] = load_streaming_dataset(\"mozilla-foundation/common_voice_11_0\", \"es\", split=\"train\", use_auth_token=True)  # set split=\"train+validation\" for low-resource\n",
+    "raw_datasets[\"test\"] = load_streaming_dataset(\"mozilla-foundation/common_voice_11_0\", \"es\", split=\"test\", use_auth_token=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d63b2d2-f68a-4d74-b7f1-5127f6d16605",
+   "metadata": {},
+   "source": [
+    "## Prepare Processor and Pre-Process Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "601c3099-1026-439e-93e2-5635b3ba5a73",
+   "metadata": {},
+   "source": [
+    "The ASR pipeline can be de-composed into three stages: \n",
+    "1) A feature extractor which pre-processes the raw audio-inputs\n",
+    "2) The model which performs the sequence-to-sequence mapping \n",
+    "3) A tokenizer which post-processes the model outputs to text format\n",
+    "\n",
+    "In 🤗 Transformers, the Whisper model has an associated feature extractor and tokenizer, \n",
+    "called [WhisperFeatureExtractor](https://huggingface.co/docs/transformers/main/model_doc/whisper#transformers.WhisperFeatureExtractor)\n",
+    "and [WhisperTokenizer](https://huggingface.co/docs/transformers/main/model_doc/whisper#transformers.WhisperTokenizer) \n",
+    "respectively. To make our lives simple, these two objects are wrapped under a single class, called the [WhisperProcessor](https://huggingface.co/docs/transformers/model_doc/whisper#transformers.WhisperProcessor). We can call the WhisperProcessor to perform \n",
+    "both the audio pre-processing and the text token post-processing. In doing so, we only need to keep track of two objects during training: \n",
+    "the `processor` and the `model`.\n",
+    "\n",
+    "If using a multilingual checkpoint, you should set the `\"language\"` to your target text language. You should also set the task to `\"transcribe\"` for speech recogntition and `\"translate\"` for speech translation. These arguments modify the behaviour of the tokenizer - they should be set correctly to ensure the target labels are encoded properly. These arguments should be omitted for English-only fine-tuning."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77d9f0c5-8607-4642-a8ac-c3ab2e223ea6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import WhisperProcessor\n",
+    "\n",
+    "processor = WhisperProcessor.from_pretrained(\"openai/whisper-small\", language=\"Spanish\", task=\"transcribe\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "381acd09-0b0f-4d04-9eb3-f028ac0e5f2c",
+   "metadata": {},
+   "source": [
+    "### Pre-Process Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf10cd3e-924e-44fc-8790-46e413de7b3d",
+   "metadata": {},
+   "source": [
+    "Let's have a look at the dataset features. Pay particular attention to the `\"audio\"` column - this details the sampling rate of our audio inputs:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ab5a13b4-9bd4-4aa0-aef2-b3de9b762988",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "raw_datasets[\"train\"].features"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a679f05-063d-41b3-9b58-4fc9c6ccf4fd",
+   "metadata": {},
+   "source": [
+    "Since our input audio is sampled at 48kHz, we need to _downsample_ it to\n",
+    "16kHz prior to passing it to the Whisper feature extractor, 16kHz being the sampling rate expected by the Whisper model. \n",
+    "\n",
+    "We'll set the audio inputs to the correct sampling rate using dataset's \n",
+    "[`cast_column`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=cast_column#datasets.DatasetDict.cast_column)\n",
+    "method. This operation does not change the audio in-place, \n",
+    "but rather signals to `datasets` to resample audio samples _on the fly_ the \n",
+    "first time that they are loaded:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3ab6a724-3d1e-478b-a9e9-d2f85feb6c39",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datasets import Audio\n",
+    "\n",
+    "raw_datasets = raw_datasets.cast_column(\"audio\", Audio(sampling_rate=16000))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "161322c2-94f3-4d26-9e1d-d9d5202ca3cf",
+   "metadata": {},
+   "source": [
+    "We'll define our pre-processing strategy. We advise that you **do not** lower-case the transcriptions or remove punctuation unless mixing different datasets. This will enable you to fine-tune Whisper models that can predict punctuation and casing. Later, you will see how we can evaluate the predictions without punctuation or casing, so that the models benefit from the WER improvement obtained by normalising the transcriptions while still predicting fully formatted transcriptions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d041650e-1c48-4439-87b3-5b6f4a514107",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers.models.whisper.english_normalizer import BasicTextNormalizer\n",
+    "\n",
+    "do_lower_case = False\n",
+    "do_remove_punctuation = False\n",
+    "\n",
+    "normalizer = BasicTextNormalizer()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bfaa935b-a11d-497c-88c1-0c4d1bb3247b",
+   "metadata": {},
+   "source": [
+    "Now we can write a function to prepare our data ready for the model:\n",
+    "1. We load and resample the audio data by calling `batch[\"audio\"]`. As explained above, 🤗 Datasets performs any necessary resampling operations on the fly.\n",
+    "2. We use the feature extractor to compute the log-Mel spectrogram input features from our 1-dimensional audio array.\n",
+    "3. We perform any optional pre-processing (lower-case or remove punctuation).\n",
+    "4. We encode the transcriptions to label ids through the use of the tokenizer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c085911c-a10a-41ef-8874-306e0503e9bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def prepare_dataset(batch):\n",
+    "    # load and (possibly) resample audio data to 16kHz\n",
+    "    audio = batch[\"audio\"]\n",
+    "\n",
+    "    # compute log-Mel input features from input audio array \n",
+    "    batch[\"input_features\"] = processor.feature_extractor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_features[0]\n",
+    "    # compute input length of audio sample in seconds\n",
+    "    batch[\"input_length\"] = len(audio[\"array\"]) / audio[\"sampling_rate\"]\n",
+    "    \n",
+    "    # optional pre-processing steps\n",
+    "    transcription = batch[\"sentence\"]\n",
+    "    if do_lower_case:\n",
+    "        transcription = transcription.lower()\n",
+    "    if do_remove_punctuation:\n",
+    "        transcription = normalizer(transcription).strip()\n",
+    "    \n",
+    "    # encode target text to label ids\n",
+    "    batch[\"labels\"] = processor.tokenizer(transcription).input_ids\n",
+    "    return batch"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70b319fb-2439-4ef6-a70d-a47bf41c4a13",
+   "metadata": {},
+   "source": [
+    "We can apply the data preparation function to all of our training examples using 🤗 Datasets' `.map` method. We'll remove all of the columns from the raw training data, leaving just the `input_features` and `labels` defined in the `prepare_dataset` function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a37a7cdb-9013-427f-8de9-6a8d0e9dc684",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vectorized_datasets = raw_datasets.map(prepare_dataset, remove_columns=list(next(iter(raw_datasets.values())).features)).with_format(\"torch\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d59b37e-4950-47ec-9e3e-2cf2ec7fc750",
+   "metadata": {},
+   "source": [
+    "We can now define how we shuffle the data in the train split. The size of the subset we load is set by the variable `buffer_size`. You can increase or decrease this depending on your memory constraints. In this example, the `buffer_size` is set to 500, meaning 500 samples are loaded before shuffling across the subset. The larger we set this value, the closer to True offline shuffling. The `seed` is set for reproducibility:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1b145699-acfc-4b1d-93a2-a2ad3d62674c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vectorized_datasets[\"train\"] = vectorized_datasets[\"train\"].shuffle(\n",
+    "    buffer_size=500,\n",
+    "    seed=0,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "666b9ef0-7909-4e1e-a419-87604d233e29",
+   "metadata": {},
+   "source": [
+    "Finally, we filter any training data with audio samples longer than 30s. These samples would otherwise be truncated by the Whisper feature-extractor which could affect the stability of training. We define a function that returns `True` for samples that are less than 30s, and `False` for those that are longer:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "01cb25ef-4bb0-4325-9461-f59198acadf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "max_input_length = 30.0\n",
+    "\n",
+    "def is_audio_in_length_range(length):\n",
+    "    return length < max_input_length"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28e37ac3-b1c5-465b-8586-7cfd8d76b0f1",
+   "metadata": {},
+   "source": [
+    "We apply our filter function to all samples of our training dataset through 🤗 Datasets' `.filter` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "333f7f6e-6053-4d3b-8924-c733c79b82ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vectorized_datasets[\"train\"] = vectorized_datasets[\"train\"].filter(\n",
+    "    is_audio_in_length_range,\n",
+    "    input_columns=[\"input_length\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "263a5a58-0239-4a25-b0df-c625fc9c5810",
+   "metadata": {},
+   "source": [
+    "## Training and Evaluation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a693e768-c5a6-453f-89a1-b601dcf7daf7",
+   "metadata": {},
+   "source": [
+    "Now that we've prepared our data, we're ready to dive into the training pipeline. \n",
+    "The [🤗 Trainer](https://huggingface.co/transformers/master/main_classes/trainer.html?highlight=trainer)\n",
+    "will do much of the heavy lifting for us. All we have to do is:\n",
+    "\n",
+    "- Define a data collator: the data collator takes our pre-processed data and prepares PyTorch tensors ready for the model.\n",
+    "\n",
+    "- Evaluation metrics: during evaluation, we want to evaluate the model using the [word error rate (WER)](https://huggingface.co/metrics/wer) metric. We need to define a `compute_metrics` function that handles this computation.\n",
+    "\n",
+    "- Load a pre-trained checkpoint: we need to load a pre-trained checkpoint and configure it correctly for training.\n",
+    "\n",
+    "- Define the training configuration: this will be used by the 🤗 Trainer to define the training schedule."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d230e6d-624c-400a-bbf5-fa660881df25",
+   "metadata": {},
+   "source": [
+    "### Define a Data Collator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04def221-0637-4a69-b242-d3f0c1d0ee78",
+   "metadata": {},
+   "source": [
+    "The data collator for a sequence-to-sequence speech model is unique in the sense that it \n",
+    "treats the `input_features` and `labels` independently: the  `input_features` must be \n",
+    "handled by the feature extractor and the `labels` by the tokenizer.\n",
+    "\n",
+    "The `input_features` are already padded to 30s and converted to a log-Mel spectrogram \n",
+    "of fixed dimension by action of the feature extractor, so all we have to do is convert the `input_features`\n",
+    "to batched PyTorch tensors. We do this using the feature extractor's `.pad` method with `return_tensors=pt`.\n",
+    "\n",
+    "The `labels` on the other hand are un-padded. We first pad the sequences\n",
+    "to the maximum length in the batch using the tokenizer's `.pad` method. The padding tokens \n",
+    "are then replaced by `-100` so that these tokens are **not** taken into account when \n",
+    "computing the loss. We then cut the BOS token from the start of the label sequence as we \n",
+    "append it later during training.\n",
+    "\n",
+    "We can leverage the `WhisperProcessor` we defined earlier to perform both the \n",
+    "feature extractor and the tokenizer operations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8326221e-ec13-4731-bb4e-51e5fc1486c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "\n",
+    "from dataclasses import dataclass\n",
+    "from typing import Any, Dict, List, Union\n",
+    "\n",
+    "@dataclass\n",
+    "class DataCollatorSpeechSeq2SeqWithPadding:\n",
+    "    processor: Any\n",
+    "\n",
+    "    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:\n",
+    "        # split inputs and labels since they have to be of different lengths and need different padding methods\n",
+    "        # first treat the audio inputs by simply returning torch tensors\n",
+    "        input_features = [{\"input_features\": feature[\"input_features\"]} for feature in features]\n",
+    "        batch = self.processor.feature_extractor.pad(input_features, return_tensors=\"pt\")\n",
+    "\n",
+    "        # get the tokenized label sequences\n",
+    "        label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n",
+    "        # pad the labels to max length\n",
+    "        labels_batch = self.processor.tokenizer.pad(label_features, return_tensors=\"pt\")\n",
+    "\n",
+    "        # replace padding with -100 to ignore loss correctly\n",
+    "        labels = labels_batch[\"input_ids\"].masked_fill(labels_batch.attention_mask.ne(1), -100)\n",
+    "\n",
+    "        # if bos token is appended in previous tokenization step,\n",
+    "        # cut bos token here as it's append later anyways\n",
+    "        if (labels[:, 0] == self.processor.tokenizer.bos_token_id).all().cpu().item():\n",
+    "            labels = labels[:, 1:]\n",
+    "\n",
+    "        batch[\"labels\"] = labels\n",
+    "\n",
+    "        return batch"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3cae7dbf-8a50-456e-a3a8-7fd005390f86",
+   "metadata": {},
+   "source": [
+    "Let's initialise the data collator we've just defined:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fc834702-c0d3-4a96-b101-7b87be32bf42",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_collator = DataCollatorSpeechSeq2SeqWithPadding(processor=processor)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d62bb2ab-750a-45e7-82e9-61d6f4805698",
+   "metadata": {},
+   "source": [
+    "### Evaluation Metrics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "66fee1a7-a44c-461e-b047-c3917221572e",
+   "metadata": {},
+   "source": [
+    "We'll use the word error rate (WER) metric, the 'de-facto' metric for assessing \n",
+    "ASR systems. For more information, refer to the WER [docs](https://huggingface.co/metrics/wer). We'll load the WER metric from 🤗 Evaluate:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b22b4011-f31f-4b57-b684-c52332f92890",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import evaluate\n",
+    "\n",
+    "metric = evaluate.load(\"wer\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "509f96d7-3f11-4f37-add9-f74a0c44f3fc",
+   "metadata": {},
+   "source": [
+    "We then simply have to define a function that takes our model \n",
+    "predictions and returns the WER metric. This function, called\n",
+    "`compute_metrics`, first replaces `-100` with the `pad_token_id`\n",
+    "in the `label_ids` (undoing the step we applied in the \n",
+    "data collator to ignore padded tokens correctly in the loss).\n",
+    "It then decodes the predicted and label ids to strings. Finally,\n",
+    "it computes the WER between the predictions and reference labels. \n",
+    "Here, we have the option of evaluating with the 'normalised' transcriptions \n",
+    "and predictions. We recommend you set this to `True` to benefit from the WER \n",
+    "improvement obtained by normalising the transcriptions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a11d1bfc-9e28-460f-a287-72d8f7bc1acb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# evaluate with the 'normalised' WER\n",
+    "do_normalize_eval = True\n",
+    "\n",
+    "def compute_metrics(pred):\n",
+    "    pred_ids = pred.predictions\n",
+    "    label_ids = pred.label_ids\n",
+    "\n",
+    "    # replace -100 with the pad_token_id\n",
+    "    label_ids[label_ids == -100] = processor.tokenizer.pad_token_id\n",
+    "\n",
+    "    # we do not want to group tokens when computing the metrics\n",
+    "    pred_str = processor.tokenizer.batch_decode(pred_ids, skip_special_tokens=True)\n",
+    "    label_str = processor.tokenizer.batch_decode(label_ids, skip_special_tokens=True)\n",
+    "\n",
+    "    if do_normalize_eval:\n",
+    "        pred_str = [normalizer(pred) for pred in pred_str]\n",
+    "        label_str = [normalizer(label) for label in label_str]\n",
+    "        # filtering step to only evaluate the samples that correspond to non-zero references:\n",
+    "        pred_str = [pred_str[i] for i in range(len(pred_str)) if len(label_str[i]) > 0]\n",
+    "        label_str = [label_str[i] for i in range(len(label_str)) if len(label_str[i]) > 0]\n",
+    "\n",
+    "    wer = 100 * metric.compute(predictions=pred_str, references=label_str)\n",
+    "\n",
+    "    return {\"wer\": wer}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "daf2a825-6d9f-4a23-b145-c37c0039075b",
+   "metadata": {},
+   "source": [
+    "### Load a Pre-Trained Checkpoint"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "437a97fa-4864-476b-8abc-f28b8166cfa5",
+   "metadata": {},
+   "source": [
+    "Now let's load the pre-trained Whisper `small` checkpoint. Again, this \n",
+    "is trivial through use of 🤗 Transformers!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5a10cc4b-07ec-4ebd-ac1d-7c601023594f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import WhisperForConditionalGeneration\n",
+    "\n",
+    "model = WhisperForConditionalGeneration.from_pretrained(\"openai/whisper-small\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a15ead5f-2277-4a39-937b-585c2497b2df",
+   "metadata": {},
+   "source": [
+    "Override generation arguments - no tokens are forced as decoder outputs (see [`forced_decoder_ids`](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate.forced_decoder_ids)), no tokens are suppressed during generation (see [`suppress_tokens`](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate.suppress_tokens)). Set `use_cache` to False since we're using gradient checkpointing, and the two are incompatible:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "62038ba3-88ed-4fce-84db-338f50dcd04f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.config.forced_decoder_ids = None\n",
+    "model.config.suppress_tokens = []\n",
+    "model.config.use_cache = False"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2178dea4-80ca-47b6-b6ea-ba1915c90c06",
+   "metadata": {},
+   "source": [
+    "### Define the Training Configuration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c21af1e9-0188-4134-ac82-defc7bdcc436",
+   "metadata": {},
+   "source": [
+    "In the final step, we define all the parameters related to training. Here, you can set the `max_steps` to train for longer. For more detail on the training arguments, refer to the Seq2SeqTrainingArguments [docs](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.Seq2SeqTrainingArguments)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0ae3e9af-97b7-4aa0-ae85-20b23b5bcb3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import Seq2SeqTrainingArguments\n",
+    "\n",
+    "training_args = Seq2SeqTrainingArguments(\n",
+    "    output_dir=\"./\",\n",
+    "    per_device_train_batch_size=64,\n",
+    "    gradient_accumulation_steps=1,  # increase by 2x for every 2x decrease in batch size\n",
+    "    learning_rate=1e-5,\n",
+    "    warmup_steps=500,\n",
+    "    max_steps=5000,\n",
+    "    gradient_checkpointing=True,\n",
+    "    fp16=True,\n",
+    "    evaluation_strategy=\"steps\",\n",
+    "    per_device_eval_batch_size=8,\n",
+    "    predict_with_generate=True,\n",
+    "    generation_max_length=225,\n",
+    "    save_steps=1000,\n",
+    "    eval_steps=1000,\n",
+    "    logging_steps=25,\n",
+    "    report_to=[\"tensorboard\"],\n",
+    "    load_best_model_at_end=True,\n",
+    "    metric_for_best_model=\"wer\",\n",
+    "    greater_is_better=False,\n",
+    "    push_to_hub=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3a944d8-3112-4552-82a0-be25988b3857",
+   "metadata": {},
+   "source": [
+    "**Note**: if one does not want to upload the model checkpoints to the Hub, \n",
+    "set `push_to_hub=False`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "393c883e-3e50-492c-bd58-f51dbf15ee56",
+   "metadata": {},
+   "source": [
+    "We then define a custom [Callback](https://huggingface.co/docs/transformers/main_classes/callback) that is called by the 🤗 Trainer on the end of each epoch. The Callback reinitialises and reshuffles the streaming dataset at the beginning of each new epoch - this gives different shuffling across our subsets for every epoch."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3ac16b62-b3c0-4c68-8f3d-9ecf471534b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import TrainerCallback\n",
+    "from transformers.trainer_pt_utils import IterableDatasetShard\n",
+    "from torch.utils.data import IterableDataset\n",
+    "\n",
+    "# trainer callback to reinitialise and reshuffle the streamable datasets at the beginning of each epoch\n",
+    "class ShuffleCallback(TrainerCallback):\n",
+    "    def on_epoch_begin(self, args, state, control, train_dataloader, **kwargs):\n",
+    "        if isinstance(train_dataloader.dataset, IterableDatasetShard):\n",
+    "            pass  # set_epoch() is handled by the Trainer\n",
+    "        elif isinstance(train_dataloader.dataset, IterableDataset):\n",
+    "            train_dataloader.dataset.set_epoch(train_dataloader.dataset._epoch + 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bac29114-d226-4f54-97cf-8718c9f94e1e",
+   "metadata": {},
+   "source": [
+    "We can forward the training arguments to the 🤗 Trainer along with our model,\n",
+    "dataset, data collator, `compute_metrics` function and custom callback:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d546d7fe-0543-479a-b708-2ebabec19493",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import Seq2SeqTrainer\n",
+    "\n",
+    "trainer = Seq2SeqTrainer(\n",
+    "    args=training_args,\n",
+    "    model=model,\n",
+    "    train_dataset=vectorized_datasets[\"train\"],\n",
+    "    eval_dataset=vectorized_datasets[\"test\"],\n",
+    "    data_collator=data_collator,\n",
+    "    compute_metrics=compute_metrics,\n",
+    "    tokenizer=processor,\n",
+    "    callbacks=[ShuffleCallback()],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67ab88c3-7091-4e51-8ad5-f5cacbe18449",
+   "metadata": {},
+   "source": [
+    "We'll save the model and processor to the output directory before training:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a1ccb9ed-cbc8-4419-91c0-651e9424b672",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.save_pretrained(training_args.output_dir)\n",
+    "processor.save_pretrained(training_args.output_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f404cf9-4345-468c-8196-4bd101d9bd51",
+   "metadata": {},
+   "source": [
+    "### Training"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e8b8d56-5a70-4f68-bd2e-f0752d0bd112",
+   "metadata": {},
+   "source": [
+    "Training will take approximately 5-10 hours depending on your GPU. The peak GPU memory for the given training configuration is approximately 36GB. \n",
+    "Depending on your GPU, it is possible that you will encounter a CUDA `\"out-of-memory\"` error when you launch training. \n",
+    "In this case, you can reduce the `per_device_train_batch_size` incrementally by factors of 2 \n",
+    "and employ [`gradient_accumulation_steps`](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.Seq2SeqTrainingArguments.gradient_accumulation_steps)\n",
+    "to compensate.\n",
+    "\n",
+    "To launch training, simply execute:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee8b7b8e-1c9a-4d77-9137-1778a629e6de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainer.train()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "747c6a6e",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "(note that training may take some time to commence as we load the first training data samples with streaming mode)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "810ced54-7187-4a06-b2fe-ba6dcca94dc3",
+   "metadata": {},
+   "source": [
+    "We can label our checkpoint with the `whisper-event` tag on push by setting the appropriate key-word arguments (kwargs):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6dd0e310-9b07-4133-ac14-2ed2d7524e22",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kwargs = {\n",
+    "    \"dataset_tags\": \"mozilla-foundation/common_voice_11_0\",\n",
+    "    \"dataset\": \"Common Voice 11.0\",  # a 'pretty' name for the training dataset\n",
+    "    \"language\": \"es\",\n",
+    "    \"model_name\": \"Whisper Small Es - Sanchit Gandhi\",  # a 'pretty' name for your model\n",
+    "    \"finetuned_from\": \"openai/whisper-small\",\n",
+    "    \"tasks\": \"automatic-speech-recognition\",\n",
+    "    \"tags\": \"whisper-event\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "090d676a-f944-4297-a938-a40eda0b2b68",
+   "metadata": {},
+   "source": [
+    "The training results can now be uploaded to the Hub. To do so, execute the `push_to_hub` command:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "95737cda-c5dd-4887-a4d0-dfcb0d61d977",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainer.push_to_hub(**kwargs)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/whisper-fine-tuning-event/fine_tune_whisper_streaming_colab.ipynb b/whisper-fine-tuning-event/fine_tune_whisper_streaming_colab.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..35ea306d11f357a4fc96a5ba8966980a19745a31
--- /dev/null
+++ b/whisper-fine-tuning-event/fine_tune_whisper_streaming_colab.ipynb
@@ -0,0 +1,1189 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6eb59f41",
+   "metadata": {
+    "colab_type": "text",
+    "id": "view-in-github"
+   },
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/sanchit-gandhi/codesnippets/blob/main/fine_tune_whisper_streaming_colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75b58048-7d14-4fc6-8085-1fc08c81b4a6",
+   "metadata": {
+    "id": "75b58048-7d14-4fc6-8085-1fc08c81b4a6"
+   },
+   "source": [
+    "# Fine-Tune Whisper With 🤗 Transformers and Streaming Mode"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbfa8ad5-4cdc-4512-9058-836cbbf65e1a",
+   "metadata": {
+    "id": "fbfa8ad5-4cdc-4512-9058-836cbbf65e1a"
+   },
+   "source": [
+    "In this Colab, we present a step-by-step guide on fine-tuning Whisper with Hugging Face 🤗 Transformers on 400 hours of speech data! Using streaming mode, we'll show how you can train a speech recongition model on any dataset, irrespective of size. With streaming mode, storage requirements are no longer a consideration: you can train a model on whatever dataset you want, even if it's download size exceeds your devices disk space. How can this be possible? It simply seems too good to be true! Well, rest assured it's not 😉 Carry on reading to find out more."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "afe0d503-ae4e-4aa7-9af4-dbcba52db41e",
+   "metadata": {
+    "id": "afe0d503-ae4e-4aa7-9af4-dbcba52db41e"
+   },
+   "source": [
+    "## Introduction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ae91ed4-9c3e-4ade-938e-f4c2dcfbfdc0",
+   "metadata": {
+    "id": "9ae91ed4-9c3e-4ade-938e-f4c2dcfbfdc0"
+   },
+   "source": [
+    "Speech recognition datasets are large. A typical speech dataset consists of approximately 100 hours of audio-transcription data, requiring upwards of 130GB of storage space for download and preparation. For most ASR researchers, this is already at the upper limit of what is feasible for disk space. So what happens when we want to train on a larger dataset? The full [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) dataset consists of 960 hours of audio data. Kensho's [SPGISpeech](https://huggingface.co/datasets/kensho/spgispeech) contains 5,000 hours of audio data. ML Commons [People's Speech](https://huggingface.co/datasets/MLCommons/peoples_speech) contains **30,000+** hours of audio data! Do we need to bite the bullet and buy additional storage? Or is there a way we can train on all of these datasets with no disk drive requirements?\n",
+    "\n",
+    "When training machine learning systems, we rarely use the entire dataset at once. We typically _batch_ our data into smaller subsets of data, and pass these incrementally through our training pipeline. This is because we train our system on an accelerator device, such as a GPU or TPU, which has a memory limit typically around 16GB. We have to fit our model, optimiser and training data all on the same accelerator device, so we usually have to divide the dataset up into smaller batches and move them from the CPU to the GPU when required.\n",
+    "\n",
+    "Consequently, we don't require the entire dataset to be downloaded at once; we simply need the batch of data that we pass to our model at any one go. We can leverage this principle of partial dataset loading when preparing our dataset: rather than downloading the entire dataset at the start, we can load each piece of data as and when we need it. For each batch, we load the relevant data from a remote server and pass it through the training pipeline. For the next batch, we load the next items and again pass them through the training pipeline. At no point do we have to save data to our disk drive, we simply load them in memory and use them in our pipeline. In doing so, we only ever need as much memory as each individual batch requires.\n",
+    "\n",
+    "This is analogous to downloading a TV show versus streaming it 📺 When we download a TV show, we download the entire video offline and save it to our disk. Compare this to when we stream a TV show. Here, we don't download any part of the video to memory, but iterate over the video file and load each part in real-time as required. It's this same principle that we can apply to our ML training pipeline! We want to iterate over the dataset and load each sample of data as required.\n",
+    "\n",
+    "While the principle of partial dataset loading sounds ideal, it also seems **pretty** difficult to do. Luckily for us, 🤗 Datasets allows us to do this with minimal code changes! We'll make use of the principle of [_streaming_](https://huggingface.co/docs/datasets/stream), depicted graphically in Figure 1. Streaming does exactly this: the data is loaded progressively as we iterate over the dataset, meaning it is only loaded as and when we need it. If you're familiar with 🤗 Transformers and Datasets, the content of this notebook will be very familiar, with some small extensions to support streaming mode."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c87f76e-47be-4a5d-bc52-7b1c2e9d4f5a",
+   "metadata": {
+    "id": "1c87f76e-47be-4a5d-bc52-7b1c2e9d4f5a"
+   },
+   "source": [
+    "<figure>\n",
+    "<img src=\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/datasets/streaming.gif\" alt=\"Trulli\" style=\"width:100%\">\n",
+    "<figcaption align = \"center\"><b>Figure 1:</b> Streaming mode. The dataset is divided into smaller subsets, with subsets loaded progressively as we iterate over the dataset. </figcaption>\n",
+    "</figure>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d44b85a2-3465-4cd5-bcca-8ddb302ab71b",
+   "metadata": {
+    "id": "d44b85a2-3465-4cd5-bcca-8ddb302ab71b",
+    "tags": []
+   },
+   "source": [
+    "## Prepare Environment"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a47bbac5-b44b-41ac-a948-1b57cec2b6f1",
+   "metadata": {
+    "id": "a47bbac5-b44b-41ac-a948-1b57cec2b6f1"
+   },
+   "source": [
+    "First of all, let's try to secure a decent GPU for our Colab! Unfortunately, it's becoming much harder to get access to a good GPU with the free version of Google Colab. However, with Google Colab Pro / Pro+ one should have no issues in being allocated a V100 or P100 GPU.\n",
+    "\n",
+    "To get a GPU, click _Runtime_ -> _Change runtime type_, then change _Hardware accelerator_ from _None_ to _GPU_."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "47686bd5-cbb1-4352-81cf-0fcf7bbd45c3",
+   "metadata": {
+    "id": "47686bd5-cbb1-4352-81cf-0fcf7bbd45c3"
+   },
+   "source": [
+    "We can verify that we've been assigned a GPU and view its specifications:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d74b38c5-a1fb-4214-b4f4-b5bf0869f169",
+   "metadata": {
+    "id": "d74b38c5-a1fb-4214-b4f4-b5bf0869f169",
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "gpu_info = !nvidia-smi\n",
+    "gpu_info = '\\n'.join(gpu_info)\n",
+    "if gpu_info.find('failed') >= 0:\n",
+    "  print('Not connected to a GPU')\n",
+    "else:\n",
+    "  print(gpu_info)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be67f92a-2f3b-4941-a1c0-5ed2de6e0a6a",
+   "metadata": {
+    "id": "be67f92a-2f3b-4941-a1c0-5ed2de6e0a6a",
+    "tags": []
+   },
+   "source": [
+    "Next, we need to update the Unix package `ffmpeg` to version 4:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "15493a84-8b7c-4b35-9aeb-2b0a57a4e937",
+   "metadata": {
+    "id": "15493a84-8b7c-4b35-9aeb-2b0a57a4e937",
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "!add-apt-repository -y ppa:jonathonf/ffmpeg-4\n",
+    "!apt update\n",
+    "!apt install -y ffmpeg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab471347-a547-4d14-9d11-f151dc9547a7",
+   "metadata": {
+    "id": "ab471347-a547-4d14-9d11-f151dc9547a7"
+   },
+   "source": [
+    "We'll employ several popular Python packages to fine-tune the Whisper model.\n",
+    "We'll use `datasets` to download and prepare our training data and \n",
+    "`transformers` to load and train our Whisper model. We'll also require\n",
+    "the `soundfile` package to pre-process audio files, `evaluate` and `jiwer` to\n",
+    "assess the performance of our model. Finally, we'll\n",
+    "use `gradio` to build a flashy demo of our fine-tuned model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e106846-3620-46aa-989d-5e35e27c8057",
+   "metadata": {
+    "id": "4e106846-3620-46aa-989d-5e35e27c8057"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install git+https://github.com/huggingface/datasets\n",
+    "!pip install git+https://github.com/huggingface/transformers\n",
+    "!pip install librosa\n",
+    "!pip install evaluate>=0.3.0\n",
+    "!pip install jiwer\n",
+    "!pip install gradio\n",
+    "!pip install more-itertools"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b185650-af09-48c6-a67b-0e4368b74b3b",
+   "metadata": {
+    "id": "5b185650-af09-48c6-a67b-0e4368b74b3b",
+    "tags": []
+   },
+   "source": [
+    "Linking the notebook to the Hugging Face Hub is straightforward - it simply requires entering your \n",
+    "Hub authentication token when prompted. Find your Hub authentication token [here](https://huggingface.co/settings/tokens):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dff27c76-575c-432b-8916-b1b810efef4a",
+   "metadata": {
+    "id": "dff27c76-575c-432b-8916-b1b810efef4a"
+   },
+   "outputs": [],
+   "source": [
+    "from huggingface_hub import notebook_login\n",
+    "\n",
+    "notebook_login()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21b6316e-8a55-4549-a154-66d3da2ab74a",
+   "metadata": {
+    "id": "21b6316e-8a55-4549-a154-66d3da2ab74a"
+   },
+   "source": [
+    "This notebook provides a guide to fine-tuning on the task of _speech recognition_, which involves learning a\n",
+    "mapping from speech to text. Speech recognition is divided into two categories: English-only or multilingual (all other languages). \n",
+    "This notebook applies to both categories, with pointers for changing between languages and datasets.\n",
+    "\n",
+    "As for our model, we'll fine-tune the Whisper model released in [September 2022](https://openai.com/blog/whisper/) by the authors \n",
+    "Alec Radford et al. from OpenAI. Whisper is an encoder-decoder model pre-trained on 680k hours of labelled audio-transcription data. \n",
+    "It achieves strong performance on many speech recognition and speech translation datasets without fine-tuning. With fine-tuning, \n",
+    "we aim to improve upon these results further, with many SoTA results up for grabs! For a full explanation on the Whisper model, the \n",
+    "reader is advised to read the blog post [Fine-Tune Whisper with 🤗 Transformers](https://huggingface.co/blog/fine-tune-whisper#introduction).\n",
+    "\n",
+    "The Whisper checkpoints come in five configurations of varying model sizes.\n",
+    "The smallest four are trained on either English-only or multilingual data.\n",
+    "The largest checkpoint is multilingual only. All nine of the pre-trained checkpoints \n",
+    "are available on the [Hugging Face Hub](https://huggingface.co/models?search=openai/whisper). The \n",
+    "checkpoints are summarised in the following table with links to the models on the Hub:\n",
+    "\n",
+    "| Size   | Layers | Width | Heads | Parameters | English-only                                         | Multilingual                                      |\n",
+    "|--------|--------|-------|-------|------------|------------------------------------------------------|---------------------------------------------------|\n",
+    "| tiny   | 4      | 384   | 6     | 39 M       | [✓](https://huggingface.co/openai/whisper-tiny.en)   | [✓](https://huggingface.co/openai/whisper-tiny.)  |\n",
+    "| base   | 6      | 512   | 8     | 74 M       | [✓](https://huggingface.co/openai/whisper-base.en)   | [✓](https://huggingface.co/openai/whisper-base)   |\n",
+    "| small  | 12     | 768   | 12    | 244 M      | [✓](https://huggingface.co/openai/whisper-small.en)  | [✓](https://huggingface.co/openai/whisper-small)  |\n",
+    "| medium | 24     | 1024  | 16    | 769 M      | [✓](https://huggingface.co/openai/whisper-medium.en) | [✓](https://huggingface.co/openai/whisper-medium) |\n",
+    "| large  | 32     | 1280  | 20    | 1550 M     | x                                                    | [✓](https://huggingface.co/openai/whisper-large)  |\n",
+    "\n",
+    "When fine-tuning on an English dataset for speech recognition, it is recommeneded to select one of the English-only checkpoints. For any other language, it is recommended to select a multilingual checkpoint.\n",
+    "\n",
+    "For demonstration purposes, we'll fine-tune the multilingual version of the \n",
+    "[`\"small\"`](https://huggingface.co/openai/whisper-small) checkpoint with 244M params (~= 1GB). \n",
+    "As for our data, we'll train and evaluate our system on 400 hours of multilingual speech recognition data\n",
+    "taken from the [Common Voice 11](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0)\n",
+    "dataset. We'll show how we can train a model on 400 hours of training data using the default disk space \n",
+    "that comes with a standard GPU device or Google Colab."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b219c9dd-39b6-4a95-b2a1-3f547a1e7bc0",
+   "metadata": {
+    "id": "b219c9dd-39b6-4a95-b2a1-3f547a1e7bc0"
+   },
+   "source": [
+    "## Load Dataset with Streaming"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b17a4763-4381-4157-ae38-b04a8b5f1c43",
+   "metadata": {
+    "id": "b17a4763-4381-4157-ae38-b04a8b5f1c43"
+   },
+   "source": [
+    "This is where the magic happens! We'll first write a wrapper function around 🤗 Datasets `load_dataset` method. This function downloads the required splits using streaming mode by forcing `streaming=True` in the `load_dataset` method. Multiple splits can be combined (interleaved) by concatenating them with the \"+\" symbol when specifying the split name, e.g. `split=train+validation` will return a single split with the training and validation splits interleaved together. The function has the same arguments and key-word arguments as 🤗 Datasets `load_dataset` method, so we can use it in exactly the same way!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "065a8cf7-e54f-4ac3-900e-609c80714fca",
+   "metadata": {
+    "id": "065a8cf7-e54f-4ac3-900e-609c80714fca"
+   },
+   "outputs": [],
+   "source": [
+    "from datasets import interleave_datasets, load_dataset\n",
+    "\n",
+    "def load_streaming_dataset(dataset_name, dataset_config_name, split, **kwargs):\n",
+    "    if \"+\" in split:\n",
+    "        # load multiple splits separated by the `+` symbol *with* streaming mode\n",
+    "        dataset_splits = [load_dataset(dataset_name, dataset_config_name, split=split_name, streaming=True, **kwargs) for split_name in split.split(\"+\")]\n",
+    "        # interleave multiple splits to form one dataset\n",
+    "        interleaved_dataset = interleave_datasets(dataset_splits)\n",
+    "        return interleaved_dataset\n",
+    "    else:\n",
+    "        # load a single split *with* streaming mode\n",
+    "        dataset = load_dataset(dataset_name, dataset_config_name, split=split, streaming=True, **kwargs)\n",
+    "        return dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "674429c5-0ab4-4adf-975b-621bb69eca38",
+   "metadata": {
+    "id": "674429c5-0ab4-4adf-975b-621bb69eca38"
+   },
+   "source": [
+    "We'll train our system on the Spanish split of [Common Voice 11](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0). We can see how much training data we have by viewing the [language page](https://commonvoice.mozilla.org/en/datasets) on the Common Voice website. The Spanish split has over 400 hours of labelled training data - that's enourmous! More than we could ever fit on a Google Colab or a standard workstation. But with streaming mode, we'll only download data as and when we need it, making training on this dataset possible!\n",
+    "\n",
+    "Since Spanish is relatively high-resource, we'll only use the `train` split for training and the `test` split for evaluation. If you're training on a low-resource language, such as the Hindi split of Common Voice 11, it's worth combining the `train` and `validation` splits to give a larger training set. You can achieve this by setting: `split=\"train+validation\"` for the training split.\n",
+    "\n",
+    "If you're using a gated dataset, like Common Voice 11, ensure you have accepted the terms of use on the Hugging Face Hub: [mozilla-foundation/common_voice_11_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0). Once you have accepted the terms, you will have full access to the dataset and be able to load the data locally."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a2787582-554f-44ce-9f38-4180a5ed6b44",
+   "metadata": {
+    "id": "a2787582-554f-44ce-9f38-4180a5ed6b44"
+   },
+   "outputs": [],
+   "source": [
+    "from datasets import IterableDatasetDict\n",
+    "\n",
+    "raw_datasets = IterableDatasetDict()\n",
+    "\n",
+    "raw_datasets[\"train\"] = load_streaming_dataset(\"mozilla-foundation/common_voice_11_0\", \"es\", split=\"train\", use_auth_token=True)  # set split=\"train+validation\" for low-resource\n",
+    "raw_datasets[\"test\"] = load_streaming_dataset(\"mozilla-foundation/common_voice_11_0\", \"es\", split=\"test\", use_auth_token=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d63b2d2-f68a-4d74-b7f1-5127f6d16605",
+   "metadata": {
+    "id": "2d63b2d2-f68a-4d74-b7f1-5127f6d16605"
+   },
+   "source": [
+    "## Prepare Processor and Pre-Process Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "601c3099-1026-439e-93e2-5635b3ba5a73",
+   "metadata": {
+    "id": "601c3099-1026-439e-93e2-5635b3ba5a73"
+   },
+   "source": [
+    "The ASR pipeline can be de-composed into three stages: \n",
+    "1) A feature extractor which pre-processes the raw audio-inputs\n",
+    "2) The model which performs the sequence-to-sequence mapping \n",
+    "3) A tokenizer which post-processes the model outputs to text format\n",
+    "\n",
+    "In 🤗 Transformers, the Whisper model has an associated feature extractor and tokenizer, \n",
+    "called [WhisperFeatureExtractor](https://huggingface.co/docs/transformers/main/model_doc/whisper#transformers.WhisperFeatureExtractor)\n",
+    "and [WhisperTokenizer](https://huggingface.co/docs/transformers/main/model_doc/whisper#transformers.WhisperTokenizer) \n",
+    "respectively. To make our lives simple, these two objects are wrapped under a single class, called the [WhisperProcessor](https://huggingface.co/docs/transformers/model_doc/whisper#transformers.WhisperProcessor). We can call the WhisperProcessor to perform \n",
+    "both the audio pre-processing and the text token post-processing. In doing so, we only need to keep track of two objects during training: \n",
+    "the `processor` and the `model`.\n",
+    "\n",
+    "If using a multilingual checkpoint, you should set the `\"language\"` to your target text language. You should also set the task to `\"transcribe\"` for speech recogntition and `\"translate\"` for speech translation. These arguments modify the behaviour of the tokenizer - they should be set correctly to ensure the target labels are encoded properly. These arguments should be omitted for English-only fine-tuning."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77d9f0c5-8607-4642-a8ac-c3ab2e223ea6",
+   "metadata": {
+    "id": "77d9f0c5-8607-4642-a8ac-c3ab2e223ea6"
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import WhisperProcessor\n",
+    "\n",
+    "processor = WhisperProcessor.from_pretrained(\"openai/whisper-small\", language=\"Spanish\", task=\"transcribe\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "381acd09-0b0f-4d04-9eb3-f028ac0e5f2c",
+   "metadata": {
+    "id": "381acd09-0b0f-4d04-9eb3-f028ac0e5f2c"
+   },
+   "source": [
+    "### Pre-Process Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf10cd3e-924e-44fc-8790-46e413de7b3d",
+   "metadata": {
+    "id": "bf10cd3e-924e-44fc-8790-46e413de7b3d"
+   },
+   "source": [
+    "Let's have a look at the dataset features. Pay particular attention to the `\"audio\"` column - this details the sampling rate of our audio inputs:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ab5a13b4-9bd4-4aa0-aef2-b3de9b762988",
+   "metadata": {
+    "id": "ab5a13b4-9bd4-4aa0-aef2-b3de9b762988"
+   },
+   "outputs": [],
+   "source": [
+    "raw_datasets[\"train\"].features"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a679f05-063d-41b3-9b58-4fc9c6ccf4fd",
+   "metadata": {
+    "id": "5a679f05-063d-41b3-9b58-4fc9c6ccf4fd"
+   },
+   "source": [
+    "Since our input audio is sampled at 48kHz, we need to _downsample_ it to\n",
+    "16kHz prior to passing it to the Whisper feature extractor, 16kHz being the sampling rate expected by the Whisper model. \n",
+    "\n",
+    "We'll set the audio inputs to the correct sampling rate using dataset's \n",
+    "[`cast_column`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=cast_column#datasets.DatasetDict.cast_column)\n",
+    "method. This operation does not change the audio in-place, \n",
+    "but rather signals to `datasets` to resample audio samples _on the fly_ the \n",
+    "first time that they are loaded:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3ab6a724-3d1e-478b-a9e9-d2f85feb6c39",
+   "metadata": {
+    "id": "3ab6a724-3d1e-478b-a9e9-d2f85feb6c39"
+   },
+   "outputs": [],
+   "source": [
+    "from datasets import Audio\n",
+    "\n",
+    "raw_datasets = raw_datasets.cast_column(\"audio\", Audio(sampling_rate=16000))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3de7804-c296-4e08-a3bb-91c8a3d9d839",
+   "metadata": {},
+   "source": [
+    "We'll define our pre-processing strategy. We advise that you **do not** lower-case the transcriptions or remove punctuation unless mixing different datasets. This will enable you to fine-tune Whisper models that can predict punctuation and casing. Later, you will see how we can evaluate the predictions without punctuation or casing, so that the models benefit from the WER improvement obtained by normalising the transcriptions while still predicting fully formatted transcriptions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c535a26e-4e16-458a-aa5c-141125264451",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers.models.whisper.english_normalizer import BasicTextNormalizer\n",
+    "\n",
+    "do_lower_case = False\n",
+    "do_remove_punctuation = False\n",
+    "\n",
+    "normalizer = BasicTextNormalizer()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28ed733a-7c0f-4166-8334-49d5353eb15b",
+   "metadata": {},
+   "source": [
+    "Now we can write a function to prepare our data ready for the model:\n",
+    "1. We load and resample the audio data by calling `batch[\"audio\"]`. As explained above, 🤗 Datasets performs any necessary resampling operations on the fly.\n",
+    "2. We use the feature extractor to compute the log-Mel spectrogram input features from our 1-dimensional audio array.\n",
+    "3. We perform any optional pre-processing (lower-case or remove punctuation).\n",
+    "4. We encode the transcriptions to label ids through the use of the tokenizer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c085911c-a10a-41ef-8874-306e0503e9bb",
+   "metadata": {
+    "id": "c085911c-a10a-41ef-8874-306e0503e9bb"
+   },
+   "outputs": [],
+   "source": [
+    "def prepare_dataset(batch):\n",
+    "    # load and (possibly) resample audio datato 16kHz\n",
+    "    audio = batch[\"audio\"]\n",
+    "\n",
+    "    # compute log-Mel input features from input audio array \n",
+    "    batch[\"input_features\"] = processor.feature_extractor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_features[0]\n",
+    "    # compute input length of audio sample in seconds\n",
+    "    batch[\"input_length\"] = len(audio[\"array\"]) / audio[\"sampling_rate\"]\n",
+    "    \n",
+    "    # optional pre-processing steps\n",
+    "    transcription = batch[\"sentence\"]\n",
+    "    if do_lower_case:\n",
+    "        transcription = transcription.lower()\n",
+    "    if do_remove_punctuation:\n",
+    "        transcription = re.sub(punctuation_to_remove_regex, \" \", transcription).strip()\n",
+    "    \n",
+    "    # encode target text to label ids\n",
+    "    batch[\"labels\"] = processor.tokenizer(transcription).input_ids\n",
+    "    return batch"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70b319fb-2439-4ef6-a70d-a47bf41c4a13",
+   "metadata": {
+    "id": "70b319fb-2439-4ef6-a70d-a47bf41c4a13"
+   },
+   "source": [
+    "We can apply the data preparation function to all of our training examples using 🤗 Datasets' `.map` method. We'll remove all of the columns from the raw training data, leaving just the `input_features` and `labels` defined in the `prepare_dataset` function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a37a7cdb-9013-427f-8de9-6a8d0e9dc684",
+   "metadata": {
+    "id": "a37a7cdb-9013-427f-8de9-6a8d0e9dc684"
+   },
+   "outputs": [],
+   "source": [
+    "vectorized_datasets = raw_datasets.map(prepare_dataset, remove_columns=list(next(iter(raw_datasets.values())).features)).with_format(\"torch\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d59b37e-4950-47ec-9e3e-2cf2ec7fc750",
+   "metadata": {
+    "id": "3d59b37e-4950-47ec-9e3e-2cf2ec7fc750"
+   },
+   "source": [
+    "We can now define how we shuffle the data in the train split. The size of the subset we load is set by the variable `buffer_size`. You can increase or decrease this depending on your memory constraints. In this example, the `buffer_size` is set to 500, meaning 500 samples are loaded before shuffling across the subset. The larger we set this value, the closer to True offline shuffling. The `seed` is set for reproducibility:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1b145699-acfc-4b1d-93a2-a2ad3d62674c",
+   "metadata": {
+    "id": "1b145699-acfc-4b1d-93a2-a2ad3d62674c"
+   },
+   "outputs": [],
+   "source": [
+    "vectorized_datasets[\"train\"] = vectorized_datasets[\"train\"].shuffle(\n",
+    "    buffer_size=500,\n",
+    "    seed=0,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "666b9ef0-7909-4e1e-a419-87604d233e29",
+   "metadata": {
+    "id": "666b9ef0-7909-4e1e-a419-87604d233e29"
+   },
+   "source": [
+    "Finally, we filter any training data with audio samples longer than 30s. These samples would otherwise be truncated by the Whisper feature-extractor which could affect the stability of training. We define a function that returns `True` for samples that are less than 30s, and `False` for those that are longer:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "01cb25ef-4bb0-4325-9461-f59198acadf6",
+   "metadata": {
+    "id": "01cb25ef-4bb0-4325-9461-f59198acadf6"
+   },
+   "outputs": [],
+   "source": [
+    "max_input_length = 30.0\n",
+    "\n",
+    "def is_audio_in_length_range(length):\n",
+    "    return length < max_input_length"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28e37ac3-b1c5-465b-8586-7cfd8d76b0f1",
+   "metadata": {
+    "id": "28e37ac3-b1c5-465b-8586-7cfd8d76b0f1"
+   },
+   "source": [
+    "We apply our filter function to all samples of our training dataset through 🤗 Datasets' `.filter` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "333f7f6e-6053-4d3b-8924-c733c79b82ac",
+   "metadata": {
+    "id": "333f7f6e-6053-4d3b-8924-c733c79b82ac"
+   },
+   "outputs": [],
+   "source": [
+    "vectorized_datasets[\"train\"] = vectorized_datasets[\"train\"].filter(\n",
+    "    is_audio_in_length_range,\n",
+    "    input_columns=[\"input_length\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "263a5a58-0239-4a25-b0df-c625fc9c5810",
+   "metadata": {
+    "id": "263a5a58-0239-4a25-b0df-c625fc9c5810"
+   },
+   "source": [
+    "## Training and Evaluation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a693e768-c5a6-453f-89a1-b601dcf7daf7",
+   "metadata": {
+    "id": "a693e768-c5a6-453f-89a1-b601dcf7daf7"
+   },
+   "source": [
+    "Now that we've prepared our data, we're ready to dive into the training pipeline. \n",
+    "The [🤗 Trainer](https://huggingface.co/transformers/master/main_classes/trainer.html?highlight=trainer)\n",
+    "will do much of the heavy lifting for us. All we have to do is:\n",
+    "\n",
+    "- Define a data collator: the data collator takes our pre-processed data and prepares PyTorch tensors ready for the model.\n",
+    "\n",
+    "- Evaluation metrics: during evaluation, we want to evaluate the model using the [word error rate (WER)](https://huggingface.co/metrics/wer) metric. We need to define a `compute_metrics` function that handles this computation.\n",
+    "\n",
+    "- Load a pre-trained checkpoint: we need to load a pre-trained checkpoint and configure it correctly for training.\n",
+    "\n",
+    "- Define the training configuration: this will be used by the 🤗 Trainer to define the training schedule."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d230e6d-624c-400a-bbf5-fa660881df25",
+   "metadata": {
+    "id": "8d230e6d-624c-400a-bbf5-fa660881df25"
+   },
+   "source": [
+    "### Define a Data Collator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04def221-0637-4a69-b242-d3f0c1d0ee78",
+   "metadata": {
+    "id": "04def221-0637-4a69-b242-d3f0c1d0ee78"
+   },
+   "source": [
+    "The data collator for a sequence-to-sequence speech model is unique in the sense that it \n",
+    "treats the `input_features` and `labels` independently: the  `input_features` must be \n",
+    "handled by the feature extractor and the `labels` by the tokenizer.\n",
+    "\n",
+    "The `input_features` are already padded to 30s and converted to a log-Mel spectrogram \n",
+    "of fixed dimension by action of the feature extractor, so all we have to do is convert the `input_features`\n",
+    "to batched PyTorch tensors. We do this using the feature extractor's `.pad` method with `return_tensors=pt`.\n",
+    "\n",
+    "The `labels` on the other hand are un-padded. We first pad the sequences\n",
+    "to the maximum length in the batch using the tokenizer's `.pad` method. The padding tokens \n",
+    "are then replaced by `-100` so that these tokens are **not** taken into account when \n",
+    "computing the loss. We then cut the BOS token from the start of the label sequence as we \n",
+    "append it later during training.\n",
+    "\n",
+    "We can leverage the `WhisperProcessor` we defined earlier to perform both the \n",
+    "feature extractor and the tokenizer operations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8326221e-ec13-4731-bb4e-51e5fc1486c5",
+   "metadata": {
+    "id": "8326221e-ec13-4731-bb4e-51e5fc1486c5"
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "\n",
+    "from dataclasses import dataclass\n",
+    "from typing import Any, Dict, List, Union\n",
+    "\n",
+    "@dataclass\n",
+    "class DataCollatorSpeechSeq2SeqWithPadding:\n",
+    "    processor: Any\n",
+    "\n",
+    "    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:\n",
+    "        # split inputs and labels since they have to be of different lengths and need different padding methods\n",
+    "        # first treat the audio inputs by simply returning torch tensors\n",
+    "        input_features = [{\"input_features\": feature[\"input_features\"]} for feature in features]\n",
+    "        batch = self.processor.feature_extractor.pad(input_features, return_tensors=\"pt\")\n",
+    "\n",
+    "        # get the tokenized label sequences\n",
+    "        label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n",
+    "        # pad the labels to max length\n",
+    "        labels_batch = self.processor.tokenizer.pad(label_features, return_tensors=\"pt\")\n",
+    "\n",
+    "        # replace padding with -100 to ignore loss correctly\n",
+    "        labels = labels_batch[\"input_ids\"].masked_fill(labels_batch.attention_mask.ne(1), -100)\n",
+    "\n",
+    "        # if bos token is appended in previous tokenization step,\n",
+    "        # cut bos token here as it's append later anyways\n",
+    "        if (labels[:, 0] == self.processor.tokenizer.bos_token_id).all().cpu().item():\n",
+    "            labels = labels[:, 1:]\n",
+    "\n",
+    "        batch[\"labels\"] = labels\n",
+    "\n",
+    "        return batch"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3cae7dbf-8a50-456e-a3a8-7fd005390f86",
+   "metadata": {
+    "id": "3cae7dbf-8a50-456e-a3a8-7fd005390f86"
+   },
+   "source": [
+    "Let's initialise the data collator we've just defined:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fc834702-c0d3-4a96-b101-7b87be32bf42",
+   "metadata": {
+    "id": "fc834702-c0d3-4a96-b101-7b87be32bf42"
+   },
+   "outputs": [],
+   "source": [
+    "data_collator = DataCollatorSpeechSeq2SeqWithPadding(processor=processor)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d62bb2ab-750a-45e7-82e9-61d6f4805698",
+   "metadata": {
+    "id": "d62bb2ab-750a-45e7-82e9-61d6f4805698"
+   },
+   "source": [
+    "### Evaluation Metrics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "66fee1a7-a44c-461e-b047-c3917221572e",
+   "metadata": {
+    "id": "66fee1a7-a44c-461e-b047-c3917221572e"
+   },
+   "source": [
+    "We'll use the word error rate (WER) metric, the 'de-facto' metric for assessing \n",
+    "ASR systems. For more information, refer to the WER [docs](https://huggingface.co/metrics/wer). We'll load the WER metric from 🤗 Evaluate:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b22b4011-f31f-4b57-b684-c52332f92890",
+   "metadata": {
+    "id": "b22b4011-f31f-4b57-b684-c52332f92890"
+   },
+   "outputs": [],
+   "source": [
+    "import evaluate\n",
+    "\n",
+    "metric = evaluate.load(\"wer\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "509f96d7-3f11-4f37-add9-f74a0c44f3fc",
+   "metadata": {
+    "id": "509f96d7-3f11-4f37-add9-f74a0c44f3fc"
+   },
+   "source": [
+    "We then simply have to define a function that takes our model \n",
+    "predictions and returns the WER metric. This function, called\n",
+    "`compute_metrics`, first replaces `-100` with the `pad_token_id`\n",
+    "in the `label_ids` (undoing the step we applied in the \n",
+    "data collator to ignore padded tokens correctly in the loss).\n",
+    "It then decodes the predicted and label ids to strings. Finally,\n",
+    "it computes the WER between the predictions and reference labels. \n",
+    "Here, we have the option of evaluating with the 'normalised' transcriptions \n",
+    "and predictions. We recommend you set this to `True` to benefit from the WER \n",
+    "improvement obtained by normalising the transcriptions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a11d1bfc-9e28-460f-a287-72d8f7bc1acb",
+   "metadata": {
+    "id": "a11d1bfc-9e28-460f-a287-72d8f7bc1acb"
+   },
+   "outputs": [],
+   "source": [
+    "# evaluate with the 'normalised' WER\n",
+    "do_normalize_eval = True\n",
+    "\n",
+    "def compute_metrics(pred):\n",
+    "    pred_ids = pred.predictions\n",
+    "    label_ids = pred.label_ids\n",
+    "\n",
+    "    # replace -100 with the pad_token_id\n",
+    "    label_ids[label_ids == -100] = processor.tokenizer.pad_token_id\n",
+    "\n",
+    "    # we do not want to group tokens when computing the metrics\n",
+    "    pred_str = processor.tokenizer.batch_decode(pred_ids, skip_special_tokens=True)\n",
+    "    label_str = processor.tokenizer.batch_decode(label_ids, skip_special_tokens=True)\n",
+    "\n",
+    "    if do_normalize_eval:\n",
+    "        pred_str = [normalizer(pred) for pred in pred_str]\n",
+    "        label_str = [normalizer(label) for label in label_str]\n",
+    "        # filtering step to only evaluate the samples that correspond to non-zero references:\n",
+    "        pred_str = [pred_str[i] for i in range(len(pred_str)) if len(label_str[i]) > 0]\n",
+    "        label_str = [label_str[i] for i in range(len(label_str)) if len(label_str[i]) > 0]\n",
+    "    \n",
+    "    wer = 100 * metric.compute(predictions=pred_str, references=label_str)\n",
+    "    return {\"wer\":wer}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "daf2a825-6d9f-4a23-b145-c37c0039075b",
+   "metadata": {
+    "id": "daf2a825-6d9f-4a23-b145-c37c0039075b"
+   },
+   "source": [
+    "### Load a Pre-Trained Checkpoint"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "437a97fa-4864-476b-8abc-f28b8166cfa5",
+   "metadata": {
+    "id": "437a97fa-4864-476b-8abc-f28b8166cfa5"
+   },
+   "source": [
+    "Now let's load the pre-trained Whisper `small` checkpoint. Again, this \n",
+    "is trivial through use of 🤗 Transformers!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5a10cc4b-07ec-4ebd-ac1d-7c601023594f",
+   "metadata": {
+    "id": "5a10cc4b-07ec-4ebd-ac1d-7c601023594f"
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import WhisperForConditionalGeneration\n",
+    "\n",
+    "model = WhisperForConditionalGeneration.from_pretrained(\"openai/whisper-small\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a15ead5f-2277-4a39-937b-585c2497b2df",
+   "metadata": {
+    "id": "a15ead5f-2277-4a39-937b-585c2497b2df"
+   },
+   "source": [
+    "Override generation arguments - no tokens are forced as decoder outputs (see [`forced_decoder_ids`](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate.forced_decoder_ids)), no tokens are suppressed during generation (see [`suppress_tokens`](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate.suppress_tokens)). Set `use_cache` to False since we're using gradient checkpointing, and the two are incompatible:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "62038ba3-88ed-4fce-84db-338f50dcd04f",
+   "metadata": {
+    "id": "62038ba3-88ed-4fce-84db-338f50dcd04f"
+   },
+   "outputs": [],
+   "source": [
+    "model.config.forced_decoder_ids = None\n",
+    "model.config.suppress_tokens = []\n",
+    "model.config.use_cache = False"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2178dea4-80ca-47b6-b6ea-ba1915c90c06",
+   "metadata": {
+    "id": "2178dea4-80ca-47b6-b6ea-ba1915c90c06"
+   },
+   "source": [
+    "### Define the Training Configuration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c21af1e9-0188-4134-ac82-defc7bdcc436",
+   "metadata": {
+    "id": "c21af1e9-0188-4134-ac82-defc7bdcc436"
+   },
+   "source": [
+    "In the final step, we define all the parameters related to training. Here, you can set the `max_steps` to train for longer. For more detail on the training arguments, refer to the Seq2SeqTrainingArguments [docs](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.Seq2SeqTrainingArguments)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0ae3e9af-97b7-4aa0-ae85-20b23b5bcb3a",
+   "metadata": {
+    "id": "0ae3e9af-97b7-4aa0-ae85-20b23b5bcb3a"
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import Seq2SeqTrainingArguments\n",
+    "\n",
+    "training_args = Seq2SeqTrainingArguments(\n",
+    "    output_dir=\"./whisper-small-es\",  # your repo name\n",
+    "    per_device_train_batch_size=64,\n",
+    "    gradient_accumulation_steps=1,  # increase by 2x for every 2x decrease in batch size\n",
+    "    learning_rate=1e-5,\n",
+    "    warmup_steps=500,\n",
+    "    max_steps=5000,\n",
+    "    gradient_checkpointing=True,\n",
+    "    fp16=True,\n",
+    "    evaluation_strategy=\"steps\",\n",
+    "    per_device_eval_batch_size=8,\n",
+    "    predict_with_generate=True,\n",
+    "    generation_max_length=225,\n",
+    "    save_steps=1000,\n",
+    "    eval_steps=1000,\n",
+    "    logging_steps=25,\n",
+    "    report_to=[\"tensorboard\"],\n",
+    "    load_best_model_at_end=True,\n",
+    "    metric_for_best_model=\"wer\",\n",
+    "    greater_is_better=False,\n",
+    "    push_to_hub=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3a944d8-3112-4552-82a0-be25988b3857",
+   "metadata": {
+    "id": "b3a944d8-3112-4552-82a0-be25988b3857"
+   },
+   "source": [
+    "**Note**: if one does not want to upload the model checkpoints to the Hub, \n",
+    "set `push_to_hub=False`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "393c883e-3e50-492c-bd58-f51dbf15ee56",
+   "metadata": {
+    "id": "393c883e-3e50-492c-bd58-f51dbf15ee56"
+   },
+   "source": [
+    "We then define a custom [Callback](https://huggingface.co/docs/transformers/main_classes/callback) that is called by the 🤗 Trainer on the end of each epoch. The Callback reinitialises and reshuffles the streaming dataset at the beginning of each new epoch - this gives different shuffling across our subsets for every epoch."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3ac16b62-b3c0-4c68-8f3d-9ecf471534b2",
+   "metadata": {
+    "id": "3ac16b62-b3c0-4c68-8f3d-9ecf471534b2"
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import TrainerCallback\n",
+    "from transformers.trainer_pt_utils import IterableDatasetShard\n",
+    "from torch.utils.data import IterableDataset\n",
+    "\n",
+    "# trainer callback to reinitialise and reshuffle the streamable datasets at the beginning of each epoch\n",
+    "class ShuffleCallback(TrainerCallback):\n",
+    "    def on_epoch_begin(self, args, state, control, train_dataloader, **kwargs):\n",
+    "        if isinstance(train_dataloader.dataset, IterableDatasetShard):\n",
+    "            pass  # set_epoch() is handled by the Trainer\n",
+    "        elif isinstance(train_dataloader.dataset, IterableDataset):\n",
+    "            train_dataloader.dataset.set_epoch(train_dataloader.dataset._epoch + 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bac29114-d226-4f54-97cf-8718c9f94e1e",
+   "metadata": {
+    "id": "bac29114-d226-4f54-97cf-8718c9f94e1e"
+   },
+   "source": [
+    "We can forward the training arguments to the 🤗 Trainer along with our model,\n",
+    "dataset, data collator, `compute_metrics` function and custom callback:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d546d7fe-0543-479a-b708-2ebabec19493",
+   "metadata": {
+    "id": "d546d7fe-0543-479a-b708-2ebabec19493"
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import Seq2SeqTrainer\n",
+    "\n",
+    "trainer = Seq2SeqTrainer(\n",
+    "    args=training_args,\n",
+    "    model=model,\n",
+    "    train_dataset=vectorized_datasets[\"train\"],\n",
+    "    eval_dataset=vectorized_datasets[\"test\"],\n",
+    "    data_collator=data_collator,\n",
+    "    compute_metrics=compute_metrics,\n",
+    "    tokenizer=processor,\n",
+    "    callbacks=[ShuffleCallback()],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67ab88c3-7091-4e51-8ad5-f5cacbe18449",
+   "metadata": {
+    "id": "67ab88c3-7091-4e51-8ad5-f5cacbe18449"
+   },
+   "source": [
+    "We'll save the model and processor to the output directory before training:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a1ccb9ed-cbc8-4419-91c0-651e9424b672",
+   "metadata": {
+    "id": "a1ccb9ed-cbc8-4419-91c0-651e9424b672"
+   },
+   "outputs": [],
+   "source": [
+    "model.save_pretrained(training_args.output_dir)\n",
+    "processor.save_pretrained(training_args.output_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f404cf9-4345-468c-8196-4bd101d9bd51",
+   "metadata": {
+    "id": "7f404cf9-4345-468c-8196-4bd101d9bd51"
+   },
+   "source": [
+    "### Training"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "994fcdfa-6074-4c04-96c2-ed8781c9dbc4",
+   "metadata": {
+    "id": "994fcdfa-6074-4c04-96c2-ed8781c9dbc4"
+   },
+   "source": [
+    "Training will take approximately 5-10 hours depending on the GPU\n",
+    "allocated to this Google Colab. If using this Google Colab directly to \n",
+    "fine-tune a Whisper model, you should make sure that training isn't \n",
+    "interrupted due to inactivity. A simple workaround to prevent this is \n",
+    "to paste the following code into the console of this tab (_right mouse click_ \n",
+    "-> _inspect_ -> _Console tab_ -> _insert code_)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ec886310-c18d-4a8d-9d6a-842a11b22d6a",
+   "metadata": {
+    "id": "ec886310-c18d-4a8d-9d6a-842a11b22d6a"
+   },
+   "source": [
+    "```javascript\n",
+    "function ConnectButton(){\n",
+    "    console.log(\"Connect pushed\"); \n",
+    "    document.querySelector(\"#top-toolbar > colab-connect-button\").shadowRoot.querySelector(\"#connect\").click() \n",
+    "}\n",
+    "setInterval(ConnectButton, 60000);\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e8b8d56-5a70-4f68-bd2e-f0752d0bd112",
+   "metadata": {
+    "id": "5e8b8d56-5a70-4f68-bd2e-f0752d0bd112"
+   },
+   "source": [
+    "The peak GPU memory for the given training configuration is approximately 36GB. \n",
+    "Depending on your GPU, it is possible that you will encounter a CUDA `\"out-of-memory\"` error when you launch training. \n",
+    "In this case, you can reduce the `per_device_train_batch_size` incrementally by factors of 2 \n",
+    "and employ [`gradient_accumulation_steps`](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.Seq2SeqTrainingArguments.gradient_accumulation_steps)\n",
+    "to compensate.\n",
+    "\n",
+    "To launch training, simply execute:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee8b7b8e-1c9a-4d77-9137-1778a629e6de",
+   "metadata": {
+    "id": "ee8b7b8e-1c9a-4d77-9137-1778a629e6de"
+   },
+   "outputs": [],
+   "source": [
+    "trainer.train()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "747c6a6e",
+   "metadata": {
+    "id": "747c6a6e",
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "(note that training may take some time to commence as we load the first training data samples with streaming mode)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "810ced54-7187-4a06-b2fe-ba6dcca94dc3",
+   "metadata": {
+    "id": "810ced54-7187-4a06-b2fe-ba6dcca94dc3"
+   },
+   "source": [
+    "We can label our checkpoint with the `whisper-event` tag on push by setting the appropriate key-word arguments (kwargs):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6dd0e310-9b07-4133-ac14-2ed2d7524e22",
+   "metadata": {
+    "id": "6dd0e310-9b07-4133-ac14-2ed2d7524e22"
+   },
+   "outputs": [],
+   "source": [
+    "kwargs = {\n",
+    "    \"dataset_tags\": \"mozilla-foundation/common_voice_11_0\",\n",
+    "    \"dataset\": \"Common Voice 11.0\",  # a 'pretty' name for the training dataset\n",
+    "    \"language\": \"es\",\n",
+    "    \"model_name\": \"Whisper Small Es - Sanchit Gandhi\",  # a 'pretty' name for your model\n",
+    "    \"finetuned_from\": \"openai/whisper-small\",\n",
+    "    \"tasks\": \"automatic-speech-recognition\",\n",
+    "    \"tags\": \"whisper-event\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "090d676a-f944-4297-a938-a40eda0b2b68",
+   "metadata": {
+    "id": "090d676a-f944-4297-a938-a40eda0b2b68"
+   },
+   "source": [
+    "The training results can now be uploaded to the Hub. To do so, execute the `push_to_hub` command:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "95737cda-c5dd-4887-a4d0-dfcb0d61d977",
+   "metadata": {
+    "id": "95737cda-c5dd-4887-a4d0-dfcb0d61d977"
+   },
+   "outputs": [],
+   "source": [
+    "trainer.push_to_hub(**kwargs)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "include_colab_link": true,
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/whisper-fine-tuning-event/interleave_streaming_datasets.ipynb b/whisper-fine-tuning-event/interleave_streaming_datasets.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..ff4f391f05bfd539ddcf2f26de29fa11198716b7
--- /dev/null
+++ b/whisper-fine-tuning-event/interleave_streaming_datasets.ipynb
@@ -0,0 +1,358 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6a5c0357",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Ensure datasets is installed from main. Uncomment the following line if you face issues running this script:\n",
+    "# !pip install git+https://github.com/huggingface/datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "794aaced",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from datasets import Audio, interleave_datasets, IterableDataset, load_dataset\n",
+    "from typing import List, Optional"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f210ca9a-486b-46a2-a675-2526a9bd83f5",
+   "metadata": {},
+   "source": [
+    "### Define the dataset attributes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc07293f-3ba4-4e89-a4ca-8e39409a8373",
+   "metadata": {},
+   "source": [
+    "In this example, we'll show to combine the Common Voice 11, VoxPopuli, Mulitlingual LibriSpeech and FLEURS datasets for Spanish, giving a training corpus equal to the sum of the individual datasets. This is particularly beneficial in low-resource settings, where any one of the datasets alone might have insufficient data to train a model.\n",
+    "\n",
+    "We need to specify the dataset names on the Hub, the corresponding configs and finally the text column names for the transcriptions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c53344f3-c315-430a-a2f3-57aea6bb0e17",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_names = [\"mozilla-foundation/common_voice_11_0\", \"facebook/voxpopuli\", \"facebook/multilingual_librispeech\", \"google/fleurs\"]\n",
+    "dataset_config_names = [\"es\", \"es\", \"spanish\", \"es_419\"]\n",
+    "text_column_names = [\"sentence\", \"normalized_text\", \"text\", \"transcription\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "215541f6-ee1c-4104-b43c-fa3f7fce0494",
+   "metadata": {},
+   "source": [
+    "### Define the merging function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b722a48b-c576-4a63-b2a2-3c264890a75f",
+   "metadata": {},
+   "source": [
+    "We define a function, `load_multiple_streaming_datasets`, that takes as argument a list of datasets, configs, splits (optional) and text column names (optional). It sets them to a specified sampling rate and interleaves them together, giving one merged dataset. This is all \n",
+    "done in _streaming mode_: as we iterate over the merged dataset we load samples one-by-one on the fly. No data is\n",
+    "saved to disk.\n",
+    "\n",
+    "We can also specify our strategy for interleaving datasets. The default strategy, `all_exhausted` is an oversampling \n",
+    "strategy. In this case, the dataset construction is stopped as soon as every samples in every dataset \n",
+    "has been added at least once. In practice, it means that if a dataset is exhausted, it will return to the \n",
+    "beginning of this dataset until the stop criterion has been reached. You can specify `stopping_strategy=first_exhausted` \n",
+    "for a subsampling strategy, i.e the dataset construction is stopped as soon one of the dataset runs out of samples. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "61eb4cb1-ee27-4270-a474-1bb33e1df65f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_multiple_streaming_datasets(\n",
+    "    dataset_names: List,\n",
+    "    dataset_config_names: List,\n",
+    "    splits: Optional[List] = None,\n",
+    "    text_column_names: Optional[List] = None,\n",
+    "    sampling_rate: Optional[int] = 16000,\n",
+    "    stopping_strategy: Optional[str] = \"all_exhausted\",\n",
+    "    **kwargs\n",
+    ") -> IterableDataset:\n",
+    "\n",
+    "    if len(dataset_names) != len(dataset_config_names):\n",
+    "        raise ValueError(\n",
+    "            f\"Ensure one config is passed for each dataset, got {len(dataset_names)} datasets and\"\n",
+    "            f\" {len(dataset_config_names)} configs.\"\n",
+    "        )\n",
+    "\n",
+    "    if splits is not None and len(splits) != len(dataset_names):\n",
+    "        raise ValueError(\n",
+    "            f\"Ensure one split is passed for each dataset, got {len(dataset_names)} datasets and {len(splits)} splits.\"\n",
+    "        )\n",
+    "\n",
+    "    if text_column_names is not None and len(text_column_names) != len(dataset_names):\n",
+    "        raise ValueError(\n",
+    "            f\"Ensure one text column name is passed for each dataset, got {len(dataset_names)} datasets and\"\n",
+    "            f\" {len(text_column_names)} text column names.\"\n",
+    "        )\n",
+    "\n",
+    "    splits = splits if splits is not None else [\"train\" for i in range(len(dataset_names))]\n",
+    "    text_column_names = (\n",
+    "        text_column_names if text_column_names is not None else [\"text\" for i in range(len(dataset_names))]\n",
+    "    )\n",
+    "\n",
+    "    all_datasets = []\n",
+    "    # iterate over the datasets we want to interleave\n",
+    "    for i, dataset_name in enumerate(dataset_names):\n",
+    "        dataset = load_dataset(dataset_name, dataset_config_names[i], split=splits[i], streaming=True, **kwargs)\n",
+    "        # resample to specified sampling rate\n",
+    "        dataset = dataset.cast_column(\"audio\", Audio(sampling_rate))\n",
+    "        #  normalise columns to [\"audio\", \"sentence\"]\n",
+    "        if text_column_names[i] != \"sentence\":\n",
+    "            dataset = dataset.rename_column(text_column_names[i], \"sentence\")\n",
+    "        dataset = dataset.remove_columns(set(dataset.features.keys()) - set([\"audio\", \"sentence\"]))\n",
+    "        all_datasets.append(dataset)\n",
+    "\n",
+    "    interleaved_dataset = interleave_datasets(all_datasets, stopping_strategy=stopping_strategy)\n",
+    "    return interleaved_dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "29bc228b-ce9b-4cee-9092-1223ddfa51ad",
+   "metadata": {},
+   "source": [
+    "Let's apply this function to load and merge our four datasets:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8ae90f83-4ecd-46a3-98be-bd75706e0d88",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = load_multiple_streaming_datasets(dataset_names, dataset_config_names=dataset_config_names, text_column_names=text_column_names, use_auth_token=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6056a693-1fb0-45f4-ad43-be5f1812c1a5",
+   "metadata": {},
+   "source": [
+    "### Iterate over the dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ffe011f-f905-4027-ab67-5c9c3b2b5ac0",
+   "metadata": {},
+   "source": [
+    "We iterate over the dataset, loading and merging samples on the fly. Let's print the transcriptions for the first 10 samples of our merged dataset:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "75b3355a-3c06-4d23-af43-2b93b1ad70b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Reading metadata...: 230467it [00:41, 5545.80it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 ¿ Qué tal a tres de cinco ?\n",
+      "1 y desde luego esa razón no puede tener que ver con la explicación surrealista que hemos escuchado más de una vez de que se trata de una conspiración izquierdista.\n",
+      "2 para exclamar con voz de acción de gracias y para contar todas tus maravillas jehová la habitación de tu casa he amado y el lugar del tabernáculo de tu gloria no juntes con los pecadores mi alma ni con los hombres de sangres mi vida\n",
+      "3 el uso de internet y de la red informática mundial permite que los estudiantes tengan acceso a la información en todo momento\n",
+      "4 vamos , quiero decir , que no soy de citas especiales .\n",
+      "5 si bien esta lista no es perfecta sí que resulta necesario que las entidades financieras refuercen sus controles.\n",
+      "6 oye oh jehová mi voz con que á ti clamo y ten misericordia de mí respóndeme mi corazón ha dicho de ti buscad mi rostro tu rostro buscaré oh jehová\n",
+      "7 los deportes de nieve en descenso como el esquí y la tablanieve son disciplinas populares que consisten en deslizarse con esquís o una tabla fijada a los pies sobre un terreno nevado\n",
+      "8 fray Lope , en aquel momento , colmaba otro vaso igual :\n",
+      "9 señora presidenta la competitividad es importante pero no puede ser el único criterio.\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i, sample in enumerate(ds):\n",
+    "    print(i, sample[\"sentence\"])\n",
+    "    if i == 9:\n",
+    "        break"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42d5ad08-b20e-4cba-a1a9-909fdbf030d4",
+   "metadata": {},
+   "source": [
+    "We can see that the transcriptions take several different formats. Those from Common Voice 11 are cased and punctuated. Those from VoxPopuli are punctuated only. Those from Multilingual LibriSpeech and FLEURS are neither cased not punctuated. We need to normalise the transcriptions to a uniform format before training our model. \n",
+    "\n",
+    "The following code cell is lifted from the Whisper training notebook: https://github.com/huggingface/community-events/blob/main/whisper-fine-tuning-event/fine-tune-whisper-streaming.ipynb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ed20e9cd-31c2-44cb-872b-333378a92fd1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/sanchitgandhi/venv/lib/python3.8/site-packages/jax/_src/lib/__init__.py:33: UserWarning: JAX on Mac ARM machines is experimental and minimally tested. Please see https://github.com/google/jax/issues/5501 in the event of problems.\n",
+      "  warnings.warn(\"JAX on Mac ARM machines is experimental and minimally tested. \"\n"
+     ]
+    }
+   ],
+   "source": [
+    "from transformers.models.whisper.english_normalizer import BasicTextNormalizer\n",
+    "\n",
+    "do_lower_case = True\n",
+    "do_remove_punctuation = True\n",
+    "\n",
+    "normalizer = BasicTextNormalizer()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01d13029-c24f-4a51-aff2-9251a2ceb4ce",
+   "metadata": {},
+   "source": [
+    "Now we define a function to normalise our transcriptions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "26e42417-4bd2-46f8-914e-3a6f9f3471ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def normalize_transcriptions(batch):\n",
+    "    # optional pre-processing steps\n",
+    "    transcription = batch[\"sentence\"]\n",
+    "    if do_lower_case:\n",
+    "        transcription = transcription.lower()\n",
+    "    if do_remove_punctuation:\n",
+    "        transcription = normalizer(transcription).strip()\n",
+    "    batch[\"sentence\"] = transcription\n",
+    "    return batch"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b1c67fe-be4b-4ee5-9a1f-0d444f2b5c62",
+   "metadata": {},
+   "source": [
+    "Let's apply the data pre-processing steps to our dataset and view the first 10 samples again:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "0babac71-9157-4d0f-a8a8-184547bdf501",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Reading metadata...: 230467it [00:32, 6984.59it/s] \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0  qué tal a tres de cinco \n",
+      "1 y desde luego esa razón no puede tener que ver con la explicación surrealista que hemos escuchado más de una vez de que se trata de una conspiración izquierdista \n",
+      "2 para exclamar con voz de acción de gracias y para contar todas tus maravillas jehová la habitación de tu casa he amado y el lugar del tabernáculo de tu gloria no juntes con los pecadores mi alma ni con los hombres de sangres mi vida\n",
+      "3 el uso de internet y de la red informática mundial permite que los estudiantes tengan acceso a la información en todo momento\n",
+      "4 vamos quiero decir que no soy de citas especiales \n",
+      "5 si bien esta lista no es perfecta sí que resulta necesario que las entidades financieras refuercen sus controles \n",
+      "6 oye oh jehová mi voz con que á ti clamo y ten misericordia de mí respóndeme mi corazón ha dicho de ti buscad mi rostro tu rostro buscaré oh jehová\n",
+      "7 los deportes de nieve en descenso como el esquí y la tablanieve son disciplinas populares que consisten en deslizarse con esquís o una tabla fijada a los pies sobre un terreno nevado\n",
+      "8 fray lope en aquel momento colmaba otro vaso igual \n",
+      "9 señora presidenta la competitividad es importante pero no puede ser el único criterio \n"
+     ]
+    }
+   ],
+   "source": [
+    "ds = ds.map(normalize_transcriptions)\n",
+    "\n",
+    "for i, sample in enumerate(ds):\n",
+    "    print(i, sample[\"sentence\"])\n",
+    "    if i == 9:\n",
+    "        break"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d135627a-a7aa-458c-94b8-57ddeae74a72",
+   "metadata": {},
+   "source": [
+    "This time the transcriptions are in a consistent format. We can use this data to fine-tune our Whisper model. Note that since we've removed punctuation and casing, the Whisper model won't learn to predict these features."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/whisper-fine-tuning-event/merges.txt b/whisper-fine-tuning-event/merges.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6038932a2a1f09a66991b1c2adae0d14066fa29e
--- /dev/null
+++ b/whisper-fine-tuning-event/merges.txt
@@ -0,0 +1,50001 @@
+#version: 0.2
+Ġ t
+Ġ a
+Ġt h
+i n
+e r
+Ġ w
+Ġ s
+o u
+Ġth e
+r e
+o n
+a t
+e n
+Ġ c
+i t
+i s
+Ġ b
+n d
+Ġ d
+Ġ m
+Ġ h
+Ġ o
+in g
+e s
+Ġ p
+Ġt o
+a n
+Ġ f
+o r
+l l
+Ġ I
+Ġ l
+Ġ y
+a r
+Ġ g
+Ġy ou
+e d
+Ġa nd
+Ġ in
+Ġo f
+a s
+Ġ n
+o m
+i c
+Ġth at
+u s
+e t
+v e
+a l
+o w
+l e
+Ġ is
+Ġ e
+Ġ it
+o t
+' s
+Ġb e
+i on
+Ġ T
+Ġw h
+Ġ A
+en t
+Ġ S
+Ġ re
+a y
+Ġw e
+Ġ on
+er e
+Ġh a
+u t
+a c
+i d
+i g
+o s
+k e
+v er
+i m
+Ġ Ð
+ĠT h
+a m
+a ll
+Ġf or
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+c h
+r o
+Ġth is
+Ġs t
+Ġ W
+Ġ u
+a d
+ou t
+i r
+l d
+c t
+Ġ k
+i f
+Ġg o
+. .
+Ð ¾
+it h
+l y
+h t
+q u
+Ġ -
+Ġd o
+Ġ j
+Ġha ve
+Ġ B
+Ġa n
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diff --git a/whisper-fine-tuning-event/model.safetensors b/whisper-fine-tuning-event/model.safetensors
new file mode 100644
index 0000000000000000000000000000000000000000..c50aabf1c96454a3da08054ea910d3df8e5f1e87
--- /dev/null
+++ b/whisper-fine-tuning-event/model.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6a4a0ac3b8facd8cfc83bf36776caa1e11294a3ea87cb665b8c3bc3e33bb1b0c
+size 966995080
diff --git a/whisper-fine-tuning-event/normalizer.json b/whisper-fine-tuning-event/normalizer.json
new file mode 100644
index 0000000000000000000000000000000000000000..dd6ae819ad738ac1a546e9f9282ef325c33b9ea0
--- /dev/null
+++ b/whisper-fine-tuning-event/normalizer.json
@@ -0,0 +1,1742 @@
+{
+  "accessorise": "accessorize",
+  "accessorised": "accessorized",
+  "accessorises": "accessorizes",
+  "accessorising": "accessorizing",
+  "acclimatisation": "acclimatization",
+  "acclimatise": "acclimatize",
+  "acclimatised": "acclimatized",
+  "acclimatises": "acclimatizes",
+  "acclimatising": "acclimatizing",
+  "accoutrements": "accouterments",
+  "aeon": "eon",
+  "aeons": "eons",
+  "aerogramme": "aerogram",
+  "aerogrammes": "aerograms",
+  "aeroplane": "airplane",
+  "aeroplanes": "airplanes",
+  "aesthete": "esthete",
+  "aesthetes": "esthetes",
+  "aesthetic": "esthetic",
+  "aesthetically": "esthetically",
+  "aesthetics": "esthetics",
+  "aetiology": "etiology",
+  "ageing": "aging",
+  "aggrandisement": "aggrandizement",
+  "agonise": "agonize",
+  "agonised": "agonized",
+  "agonises": "agonizes",
+  "agonising": "agonizing",
+  "agonisingly": "agonizingly",
+  "almanack": "almanac",
+  "almanacks": "almanacs",
+  "aluminium": "aluminum",
+  "amortisable": "amortizable",
+  "amortisation": "amortization",
+  "amortisations": "amortizations",
+  "amortise": "amortize",
+  "amortised": "amortized",
+  "amortises": "amortizes",
+  "amortising": "amortizing",
+  "amphitheatre": "amphitheater",
+  "amphitheatres": "amphitheaters",
+  "anaemia": "anemia",
+  "anaemic": "anemic",
+  "anaesthesia": "anesthesia",
+  "anaesthetic": "anesthetic",
+  "anaesthetics": "anesthetics",
+  "anaesthetise": "anesthetize",
+  "anaesthetised": "anesthetized",
+  "anaesthetises": "anesthetizes",
+  "anaesthetising": "anesthetizing",
+  "anaesthetist": "anesthetist",
+  "anaesthetists": "anesthetists",
+  "anaesthetize": "anesthetize",
+  "anaesthetized": "anesthetized",
+  "anaesthetizes": "anesthetizes",
+  "anaesthetizing": "anesthetizing",
+  "analogue": "analog",
+  "analogues": "analogs",
+  "analyse": "analyze",
+  "analysed": "analyzed",
+  "analyses": "analyzes",
+  "analysing": "analyzing",
+  "anglicise": "anglicize",
+  "anglicised": "anglicized",
+  "anglicises": "anglicizes",
+  "anglicising": "anglicizing",
+  "annualised": "annualized",
+  "antagonise": "antagonize",
+  "antagonised": "antagonized",
+  "antagonises": "antagonizes",
+  "antagonising": "antagonizing",
+  "apologise": "apologize",
+  "apologised": "apologized",
+  "apologises": "apologizes",
+  "apologising": "apologizing",
+  "appal": "appall",
+  "appals": "appalls",
+  "appetiser": "appetizer",
+  "appetisers": "appetizers",
+  "appetising": "appetizing",
+  "appetisingly": "appetizingly",
+  "arbour": "arbor",
+  "arbours": "arbors",
+  "archaeologically": "archeologically",
+  "archaeologist": "archeologist",
+  "archaeologists": "archeologists",
+  "archaeology": "archeology</span>",
+  "archeological": "archaeological",
+  "ardour": "ardor",
+  "armour": "armor",
+  "armoured": "armored",
+  "armourer": "armorer",
+  "armourers": "armorers",
+  "armouries": "armories",
+  "armoury": "armory",
+  "artefact": "artifact",
+  "artefacts": "artifacts",
+  "authorise": "authorize",
+  "authorised": "authorized",
+  "authorises": "authorizes",
+  "authorising": "authorizing",
+  "axe": "ax",
+  "backpedalled": "backpedaled",
+  "backpedalling": "backpedaling",
+  "bannister": "banister",
+  "bannisters": "banisters",
+  "baptise": "baptize",
+  "baptised": "baptized",
+  "baptises": "baptizes",
+  "baptising": "baptizing",
+  "bastardise": "bastardize",
+  "bastardised": "bastardized",
+  "bastardises": "bastardizes",
+  "bastardising": "bastardizing",
+  "battleax": "battleaxe",
+  "baulk": "balk",
+  "baulked": "balked",
+  "baulking": "balking",
+  "baulks": "balks",
+  "bedevilled": "bedeviled",
+  "bedevilling": "bedeviling",
+  "behaviour": "behavior",
+  "behavioural": "behavioral",
+  "behaviourism": "behaviorism",
+  "behaviourist": "behaviorist",
+  "behaviourists": "behaviorists",
+  "behaviours": "behaviors",
+  "behove": "behoove",
+  "behoved": "behooved",
+  "behoves": "behooves",
+  "bejewelled": "bejeweled",
+  "belabour": "belabor",
+  "belaboured": "belabored",
+  "belabouring": "belaboring",
+  "belabours": "belabors",
+  "bevelled": "beveled",
+  "bevvies": "bevies",
+  "bevvy": "bevy",
+  "biassed": "biased",
+  "biassing": "biasing",
+  "bingeing": "binging",
+  "bougainvillaea": "bougainvillea",
+  "bougainvillaeas": "bougainvilleas",
+  "bowdlerise": "bowdlerize",
+  "bowdlerised": "bowdlerized",
+  "bowdlerises": "bowdlerizes",
+  "bowdlerising": "bowdlerizing",
+  "breathalyse": "breathalyze",
+  "breathalysed": "breathalyzed",
+  "breathalyser": "breathalyzer",
+  "breathalysers": "breathalyzers",
+  "breathalyses": "breathalyzes",
+  "breathalysing": "breathalyzing",
+  "brutalise": "brutalize",
+  "brutalised": "brutalized",
+  "brutalises": "brutalizes",
+  "brutalising": "brutalizing",
+  "busses": "buses",
+  "bussing": "busing",
+  "caesarean": "cesarean",
+  "caesareans": "cesareans",
+  "calibre": "caliber",
+  "calibres": "calibers",
+  "calliper": "caliper",
+  "callipers": "calipers",
+  "callisthenics": "calisthenics",
+  "canalise": "canalize",
+  "canalised": "canalized",
+  "canalises": "canalizes",
+  "canalising": "canalizing",
+  "cancelation": "cancellation",
+  "cancelations": "cancellations",
+  "cancelled": "canceled",
+  "cancelling": "canceling",
+  "candour": "candor",
+  "cannibalise": "cannibalize",
+  "cannibalised": "cannibalized",
+  "cannibalises": "cannibalizes",
+  "cannibalising": "cannibalizing",
+  "canonise": "canonize",
+  "canonised": "canonized",
+  "canonises": "canonizes",
+  "canonising": "canonizing",
+  "capitalise": "capitalize",
+  "capitalised": "capitalized",
+  "capitalises": "capitalizes",
+  "capitalising": "capitalizing",
+  "caramelise": "caramelize",
+  "caramelised": "caramelized",
+  "caramelises": "caramelizes",
+  "caramelising": "caramelizing",
+  "carbonise": "carbonize",
+  "carbonised": "carbonized",
+  "carbonises": "carbonizes",
+  "carbonising": "carbonizing",
+  "carolled": "caroled",
+  "carolling": "caroling",
+  "catalogue": "catalog",
+  "catalogued": "cataloged",
+  "catalogues": "catalogs",
+  "cataloguing": "cataloging",
+  "catalyse": "catalyze",
+  "catalysed": "catalyzed",
+  "catalyses": "catalyzes",
+  "catalysing": "catalyzing",
+  "categorise": "categorize",
+  "categorised": "categorized",
+  "categorises": "categorizes",
+  "categorising": "categorizing",
+  "cauterise": "cauterize",
+  "cauterised": "cauterized",
+  "cauterises": "cauterizes",
+  "cauterising": "cauterizing",
+  "cavilled": "caviled",
+  "cavilling": "caviling",
+  "centigramme": "centigram",
+  "centigrammes": "centigrams",
+  "centilitre": "centiliter",
+  "centilitres": "centiliters",
+  "centimetre": "centimeter",
+  "centimetres": "centimeters",
+  "centralise": "centralize",
+  "centralised": "centralized",
+  "centralises": "centralizes",
+  "centralising": "centralizing",
+  "centre": "center",
+  "centred": "centered",
+  "centrefold": "centerfold",
+  "centrefolds": "centerfolds",
+  "centrepiece": "centerpiece",
+  "centrepieces": "centerpieces",
+  "centres": "centers",
+  "channelled": "channeled",
+  "channelling": "channeling",
+  "characterise": "characterize",
+  "characterised": "characterized",
+  "characterises": "characterizes",
+  "characterising": "characterizing",
+  "cheque": "check",
+  "chequebook": "checkbook",
+  "chequebooks": "checkbooks",
+  "chequered": "checkered",
+  "cheques": "checks",
+  "chilli": "chili",
+  "chimaera": "chimera",
+  "chimaeras": "chimeras",
+  "chiselled": "chiseled",
+  "chiselling": "chiseling",
+  "circularise": "circularize",
+  "circularised": "circularized",
+  "circularises": "circularizes",
+  "circularising": "circularizing",
+  "civilise": "civilize",
+  "civilised": "civilized",
+  "civilises": "civilizes",
+  "civilising": "civilizing",
+  "clamour": "clamor",
+  "clamoured": "clamored",
+  "clamouring": "clamoring",
+  "clamours": "clamors",
+  "clangour": "clangor",
+  "clarinettist": "clarinetist",
+  "clarinettists": "clarinetists",
+  "collectivise": "collectivize",
+  "collectivised": "collectivized",
+  "collectivises": "collectivizes",
+  "collectivising": "collectivizing",
+  "colonisation": "colonization",
+  "colonise": "colonize",
+  "colonised": "colonized",
+  "coloniser": "colonizer",
+  "colonisers": "colonizers",
+  "colonises": "colonizes",
+  "colonising": "colonizing",
+  "colour": "color",
+  "colourant": "colorant",
+  "colourants": "colorants",
+  "coloured": "colored",
+  "coloureds": "coloreds",
+  "colourful": "colorful",
+  "colourfully": "colorfully",
+  "colouring": "coloring",
+  "colourize": "colorize",
+  "colourized": "colorized",
+  "colourizes": "colorizes",
+  "colourizing": "colorizing",
+  "colourless": "colorless",
+  "colours": "colors",
+  "commercialise": "commercialize",
+  "commercialised": "commercialized",
+  "commercialises": "commercializes",
+  "commercialising": "commercializing",
+  "compartmentalise": "compartmentalize",
+  "compartmentalised": "compartmentalized",
+  "compartmentalises": "compartmentalizes",
+  "compartmentalising": "compartmentalizing",
+  "computerise": "computerize",
+  "computerised": "computerized",
+  "computerises": "computerizes",
+  "computerising": "computerizing",
+  "conceptualise": "conceptualize",
+  "conceptualised": "conceptualized",
+  "conceptualises": "conceptualizes",
+  "conceptualising": "conceptualizing",
+  "connexion": "connection",
+  "connexions": "connections",
+  "contextualise": "contextualize",
+  "contextualised": "contextualized",
+  "contextualises": "contextualizes",
+  "contextualising": "contextualizing",
+  "cosier": "cozier",
+  "cosies": "cozies",
+  "cosiest": "coziest",
+  "cosily": "cozily",
+  "cosiness": "coziness",
+  "cosy": "cozy",
+  "councillor": "councilor",
+  "councillors": "councilors",
+  "counselled": "counseled",
+  "counselling": "counseling",
+  "counsellor": "counselor",
+  "counsellors": "counselors",
+  "crenelated": "crenellated",
+  "criminalise": "criminalize",
+  "criminalised": "criminalized",
+  "criminalises": "criminalizes",
+  "criminalising": "criminalizing",
+  "criticise": "criticize",
+  "criticised": "criticized",
+  "criticises": "criticizes",
+  "criticising": "criticizing",
+  "crueller": "crueler",
+  "cruellest": "cruelest",
+  "crystallisation": "crystallization",
+  "crystallise": "crystallize",
+  "crystallised": "crystallized",
+  "crystallises": "crystallizes",
+  "crystallising": "crystallizing",
+  "cudgelled": "cudgeled",
+  "cudgelling": "cudgeling",
+  "customise": "customize",
+  "customised": "customized",
+  "customises": "customizes",
+  "customising": "customizing",
+  "cypher": "cipher",
+  "cyphers": "ciphers",
+  "decentralisation": "decentralization",
+  "decentralise": "decentralize",
+  "decentralised": "decentralized",
+  "decentralises": "decentralizes",
+  "decentralising": "decentralizing",
+  "decriminalisation": "decriminalization",
+  "decriminalise": "decriminalize",
+  "decriminalised": "decriminalized",
+  "decriminalises": "decriminalizes",
+  "decriminalising": "decriminalizing",
+  "defence": "defense",
+  "defenceless": "defenseless",
+  "defences": "defenses",
+  "dehumanisation": "dehumanization",
+  "dehumanise": "dehumanize",
+  "dehumanised": "dehumanized",
+  "dehumanises": "dehumanizes",
+  "dehumanising": "dehumanizing",
+  "demeanour": "demeanor",
+  "demilitarisation": "demilitarization",
+  "demilitarise": "demilitarize",
+  "demilitarised": "demilitarized",
+  "demilitarises": "demilitarizes",
+  "demilitarising": "demilitarizing",
+  "demobilisation": "demobilization",
+  "demobilise": "demobilize",
+  "demobilised": "demobilized",
+  "demobilises": "demobilizes",
+  "demobilising": "demobilizing",
+  "democratisation": "democratization",
+  "democratise": "democratize",
+  "democratised": "democratized",
+  "democratises": "democratizes",
+  "democratising": "democratizing",
+  "demonise": "demonize",
+  "demonised": "demonized",
+  "demonises": "demonizes",
+  "demonising": "demonizing",
+  "demoralisation": "demoralization",
+  "demoralise": "demoralize",
+  "demoralised": "demoralized",
+  "demoralises": "demoralizes",
+  "demoralising": "demoralizing",
+  "denationalisation": "denationalization",
+  "denationalise": "denationalize",
+  "denationalised": "denationalized",
+  "denationalises": "denationalizes",
+  "denationalising": "denationalizing",
+  "deodorise": "deodorize",
+  "deodorised": "deodorized",
+  "deodorises": "deodorizes",
+  "deodorising": "deodorizing",
+  "depersonalise": "depersonalize",
+  "depersonalised": "depersonalized",
+  "depersonalises": "depersonalizes",
+  "depersonalising": "depersonalizing",
+  "deputise": "deputize",
+  "deputised": "deputized",
+  "deputises": "deputizes",
+  "deputising": "deputizing",
+  "desensitisation": "desensitization",
+  "desensitise": "desensitize",
+  "desensitised": "desensitized",
+  "desensitises": "desensitizes",
+  "desensitising": "desensitizing",
+  "destabilisation": "destabilization",
+  "destabilise": "destabilize",
+  "destabilised": "destabilized",
+  "destabilises": "destabilizes",
+  "destabilising": "destabilizing",
+  "dialled": "dialed",
+  "dialling": "dialing",
+  "dialogue": "dialog",
+  "dialogues": "dialogs",
+  "diarrhoea": "diarrhea",
+  "digitise": "digitize",
+  "digitised": "digitized",
+  "digitises": "digitizes",
+  "digitising": "digitizing",
+  "disc": "disk",
+  "discolour": "discolor",
+  "discoloured": "discolored",
+  "discolouring": "discoloring",
+  "discolours": "discolors",
+  "discs": "disks",
+  "disembowelled": "disemboweled",
+  "disembowelling": "disemboweling",
+  "disfavour": "disfavor",
+  "dishevelled": "disheveled",
+  "dishonour": "dishonor",
+  "dishonourable": "dishonorable",
+  "dishonourably": "dishonorably",
+  "dishonoured": "dishonored",
+  "dishonouring": "dishonoring",
+  "dishonours": "dishonors",
+  "disorganisation": "disorganization",
+  "disorganised": "disorganized",
+  "distil": "distill",
+  "distils": "distills",
+  "dramatisation": "dramatization",
+  "dramatisations": "dramatizations",
+  "dramatise": "dramatize",
+  "dramatised": "dramatized",
+  "dramatises": "dramatizes",
+  "dramatising": "dramatizing",
+  "draught": "draft",
+  "draughtboard": "draftboard",
+  "draughtboards": "draftboards",
+  "draughtier": "draftier",
+  "draughtiest": "draftiest",
+  "draughts": "drafts",
+  "draughtsman": "draftsman",
+  "draughtsmanship": "draftsmanship",
+  "draughtsmen": "draftsmen",
+  "draughtswoman": "draftswoman",
+  "draughtswomen": "draftswomen",
+  "draughty": "drafty",
+  "drivelled": "driveled",
+  "drivelling": "driveling",
+  "duelled": "dueled",
+  "duelling": "dueling",
+  "economise": "economize",
+  "economised": "economized",
+  "economises": "economizes",
+  "economising": "economizing",
+  "editorialise": "editorialize",
+  "editorialised": "editorialized",
+  "editorialises": "editorializes",
+  "editorialising": "editorializing",
+  "edoema": "edema",
+  "empathise": "empathize",
+  "empathised": "empathized",
+  "empathises": "empathizes",
+  "empathising": "empathizing",
+  "emphasise": "emphasize",
+  "emphasised": "emphasized",
+  "emphasises": "emphasizes",
+  "emphasising": "emphasizing",
+  "enamelled": "enameled",
+  "enamelling": "enameling",
+  "enamoured": "enamored",
+  "encyclopaedia": "encyclopedia",
+  "encyclopaedias": "encyclopedias",
+  "encyclopaedic": "encyclopedic",
+  "endeavour": "endeavor",
+  "endeavoured": "endeavored",
+  "endeavouring": "endeavoring",
+  "endeavours": "endeavors",
+  "energise": "energize",
+  "energised": "energized",
+  "energises": "energizes",
+  "energising": "energizing",
+  "enrol": "enroll",
+  "enrols": "enrolls",
+  "enthral": "enthrall",
+  "enthrals": "enthralls",
+  "epaulette": "epaulet",
+  "epaulettes": "epaulets",
+  "epicentre": "epicenter",
+  "epicentres": "epicenters",
+  "epilogue": "epilog",
+  "epilogues": "epilogs",
+  "epitomise": "epitomize",
+  "epitomised": "epitomized",
+  "epitomises": "epitomizes",
+  "epitomising": "epitomizing",
+  "equalisation": "equalization",
+  "equalise": "equalize",
+  "equalised": "equalized",
+  "equaliser": "equalizer",
+  "equalisers": "equalizers",
+  "equalises": "equalizes",
+  "equalising": "equalizing",
+  "eulogise": "eulogize",
+  "eulogised": "eulogized",
+  "eulogises": "eulogizes",
+  "eulogising": "eulogizing",
+  "evangelise": "evangelize",
+  "evangelised": "evangelized",
+  "evangelises": "evangelizes",
+  "evangelising": "evangelizing",
+  "exorcise": "exorcize",
+  "exorcised": "exorcized",
+  "exorcises": "exorcizes",
+  "exorcising": "exorcizing",
+  "extemporisation": "extemporization",
+  "extemporise": "extemporize",
+  "extemporised": "extemporized",
+  "extemporises": "extemporizes",
+  "extemporising": "extemporizing",
+  "externalisation": "externalization",
+  "externalisations": "externalizations",
+  "externalise": "externalize",
+  "externalised": "externalized",
+  "externalises": "externalizes",
+  "externalising": "externalizing",
+  "factorise": "factorize",
+  "factorised": "factorized",
+  "factorises": "factorizes",
+  "factorising": "factorizing",
+  "faecal": "fecal",
+  "faeces": "feces",
+  "familiarisation": "familiarization",
+  "familiarise": "familiarize",
+  "familiarised": "familiarized",
+  "familiarises": "familiarizes",
+  "familiarising": "familiarizing",
+  "fantasise": "fantasize",
+  "fantasised": "fantasized",
+  "fantasises": "fantasizes",
+  "fantasising": "fantasizing",
+  "favour": "favor",
+  "favourable": "favorable",
+  "favourably": "favorably",
+  "favoured": "favored",
+  "favouring": "favoring",
+  "favourite": "favorite",
+  "favourites": "favorites",
+  "favouritism": "favoritism",
+  "favours": "favors",
+  "feminise": "feminize",
+  "feminised": "feminized",
+  "feminises": "feminizes",
+  "feminising": "feminizing",
+  "fertilisation": "fertilization",
+  "fertilise": "fertilize",
+  "fertilised": "fertilized",
+  "fertiliser": "fertilizer",
+  "fertilisers": "fertilizers",
+  "fertilises": "fertilizes",
+  "fertilising": "fertilizing",
+  "fervour": "fervor",
+  "fibre": "fiber",
+  "fibreglass": "fiberglass",
+  "fibres": "fibers",
+  "fictionalisation": "fictionalization",
+  "fictionalisations": "fictionalizations",
+  "fictionalise": "fictionalize",
+  "fictionalised": "fictionalized",
+  "fictionalises": "fictionalizes",
+  "fictionalising": "fictionalizing",
+  "fillet": "filet",
+  "filleted": "fileted",
+  "filleting": "fileting",
+  "fillets": "filets",
+  "finalisation": "finalization",
+  "finalise": "finalize",
+  "finalised": "finalized",
+  "finalises": "finalizes",
+  "finalising": "finalizing",
+  "flautist": "flutist",
+  "flautists": "flutists",
+  "flavour": "flavor",
+  "flavoured": "flavored",
+  "flavouring": "flavoring",
+  "flavourings": "flavorings",
+  "flavourless": "flavorless",
+  "flavours": "flavors",
+  "flavoursome": "flavorsome",
+  "flyer / flier": "flier / flyer",
+  "foetal": "fetal",
+  "foetid": "fetid",
+  "foetus": "fetus",
+  "foetuses": "fetuses",
+  "formalisation": "formalization",
+  "formalise": "formalize",
+  "formalised": "formalized",
+  "formalises": "formalizes",
+  "formalising": "formalizing",
+  "fossilisation": "fossilization",
+  "fossilise": "fossilize",
+  "fossilised": "fossilized",
+  "fossilises": "fossilizes",
+  "fossilising": "fossilizing",
+  "fraternisation": "fraternization",
+  "fraternise": "fraternize",
+  "fraternised": "fraternized",
+  "fraternises": "fraternizes",
+  "fraternising": "fraternizing",
+  "fulfil": "fulfill",
+  "fulfilment": "fulfillment",
+  "fulfils": "fulfills",
+  "funnelled": "funneled",
+  "funnelling": "funneling",
+  "gage": "gauge",
+  "gaged": "gauged",
+  "gages": "gauges",
+  "gaging": "gauging",
+  "galvanise": "galvanize",
+  "galvanised": "galvanized",
+  "galvanises": "galvanizes",
+  "galvanising": "galvanizing",
+  "gambolled": "gamboled",
+  "gambolling": "gamboling",
+  "gaol": "jail",
+  "gaolbird": "jailbird",
+  "gaolbirds": "jailbirds",
+  "gaolbreak": "jailbreak",
+  "gaolbreaks": "jailbreaks",
+  "gaoled": "jailed",
+  "gaoler": "jailer",
+  "gaolers": "jailers",
+  "gaoling": "jailing",
+  "gaols": "jails",
+  "gasses": "gases",
+  "generalisation": "generalization",
+  "generalisations": "generalizations",
+  "generalise": "generalize",
+  "generalised": "generalized",
+  "generalises": "generalizes",
+  "generalising": "generalizing",
+  "ghettoise": "ghettoize",
+  "ghettoised": "ghettoized",
+  "ghettoises": "ghettoizes",
+  "ghettoising": "ghettoizing",
+  "gipsies": "gypsies",
+  "glamor": "glamour",
+  "glamorise": "glamorize",
+  "glamorised": "glamorized",
+  "glamorises": "glamorizes",
+  "glamorising": "glamorizing",
+  "globalisation": "globalization",
+  "globalise": "globalize",
+  "globalised": "globalized",
+  "globalises": "globalizes",
+  "globalising": "globalizing",
+  "glueing": "gluing",
+  "goitre": "goiter",
+  "goitres": "goiters",
+  "gonorrhoea": "gonorrhea",
+  "gramme": "gram",
+  "grammes": "grams",
+  "gravelled": "graveled",
+  "grey": "gray",
+  "greyed": "grayed",
+  "greying": "graying",
+  "greyish": "grayish",
+  "greyness": "grayness",
+  "greys": "grays",
+  "grovelled": "groveled",
+  "grovelling": "groveling",
+  "groyne": "groin",
+  "groynes": "groins",
+  "gruelling": "grueling",
+  "gruellingly": "gruelingly",
+  "gryphon": "griffin",
+  "gryphons": "griffins",
+  "gynaecological": "gynecological",
+  "gynaecologist": "gynecologist",
+  "gynaecologists": "gynecologists",
+  "gynaecology": "gynecology",
+  "haematological": "hematological",
+  "haematologist": "hematologist",
+  "haematologists": "hematologists",
+  "haematology": "hematology",
+  "haemoglobin": "hemoglobin",
+  "haemophilia": "hemophilia",
+  "haemophiliac": "hemophiliac",
+  "haemophiliacs": "hemophiliacs",
+  "haemorrhage": "hemorrhage",
+  "haemorrhaged": "hemorrhaged",
+  "haemorrhages": "hemorrhages",
+  "haemorrhaging": "hemorrhaging",
+  "haemorrhoids": "hemorrhoids",
+  "harbour": "harbor",
+  "harboured": "harbored",
+  "harbouring": "harboring",
+  "harbours": "harbors",
+  "harmonisation": "harmonization",
+  "harmonise": "harmonize",
+  "harmonised": "harmonized",
+  "harmonises": "harmonizes",
+  "harmonising": "harmonizing",
+  "homoeopath": "homeopath",
+  "homoeopathic": "homeopathic",
+  "homoeopaths": "homeopaths",
+  "homoeopathy": "homeopathy",
+  "homogenise": "homogenize",
+  "homogenised": "homogenized",
+  "homogenises": "homogenizes",
+  "homogenising": "homogenizing",
+  "honour": "honor",
+  "honourable": "honorable",
+  "honourably": "honorably",
+  "honoured": "honored",
+  "honouring": "honoring",
+  "honours": "honors",
+  "hospitalisation": "hospitalization",
+  "hospitalise": "hospitalize",
+  "hospitalised": "hospitalized",
+  "hospitalises": "hospitalizes",
+  "hospitalising": "hospitalizing",
+  "humanise": "humanize",
+  "humanised": "humanized",
+  "humanises": "humanizes",
+  "humanising": "humanizing",
+  "humour": "humor",
+  "humoured": "humored",
+  "humouring": "humoring",
+  "humourless": "humorless",
+  "humours": "humors",
+  "hybridise": "hybridize",
+  "hybridised": "hybridized",
+  "hybridises": "hybridizes",
+  "hybridising": "hybridizing",
+  "hypnotise": "hypnotize",
+  "hypnotised": "hypnotized",
+  "hypnotises": "hypnotizes",
+  "hypnotising": "hypnotizing",
+  "hypothesise": "hypothesize",
+  "hypothesised": "hypothesized",
+  "hypothesises": "hypothesizes",
+  "hypothesising": "hypothesizing",
+  "idealisation": "idealization",
+  "idealise": "idealize",
+  "idealised": "idealized",
+  "idealises": "idealizes",
+  "idealising": "idealizing",
+  "idolise": "idolize",
+  "idolised": "idolized",
+  "idolises": "idolizes",
+  "idolising": "idolizing",
+  "immobilisation": "immobilization",
+  "immobilise": "immobilize",
+  "immobilised": "immobilized",
+  "immobiliser": "immobilizer",
+  "immobilisers": "immobilizers",
+  "immobilises": "immobilizes",
+  "immobilising": "immobilizing",
+  "immortalise": "immortalize",
+  "immortalised": "immortalized",
+  "immortalises": "immortalizes",
+  "immortalising": "immortalizing",
+  "immunisation": "immunization",
+  "immunise": "immunize",
+  "immunised": "immunized",
+  "immunises": "immunizes",
+  "immunising": "immunizing",
+  "impanelled": "impaneled",
+  "impanelling": "impaneling",
+  "imperilled": "imperiled",
+  "imperilling": "imperiling",
+  "individualise": "individualize",
+  "individualised": "individualized",
+  "individualises": "individualizes",
+  "individualising": "individualizing",
+  "industrialise": "industrialize",
+  "industrialised": "industrialized",
+  "industrialises": "industrializes",
+  "industrialising": "industrializing",
+  "inflexion": "inflection",
+  "inflexions": "inflections",
+  "initialise": "initialize",
+  "initialised": "initialized",
+  "initialises": "initializes",
+  "initialising": "initializing",
+  "initialled": "initialed",
+  "initialling": "initialing",
+  "instal": "install",
+  "instalment": "installment",
+  "instalments": "installments",
+  "instals": "installs",
+  "instil": "instill",
+  "instils": "instills",
+  "institutionalisation": "institutionalization",
+  "institutionalise": "institutionalize",
+  "institutionalised": "institutionalized",
+  "institutionalises": "institutionalizes",
+  "institutionalising": "institutionalizing",
+  "intellectualise": "intellectualize",
+  "intellectualised": "intellectualized",
+  "intellectualises": "intellectualizes",
+  "intellectualising": "intellectualizing",
+  "internalisation": "internalization",
+  "internalise": "internalize",
+  "internalised": "internalized",
+  "internalises": "internalizes",
+  "internalising": "internalizing",
+  "internationalisation": "internationalization",
+  "internationalise": "internationalize",
+  "internationalised": "internationalized",
+  "internationalises": "internationalizes",
+  "internationalising": "internationalizing",
+  "ionisation": "ionization",
+  "ionise": "ionize",
+  "ionised": "ionized",
+  "ioniser": "ionizer",
+  "ionisers": "ionizers",
+  "ionises": "ionizes",
+  "ionising": "ionizing",
+  "italicise": "italicize",
+  "italicised": "italicized",
+  "italicises": "italicizes",
+  "italicising": "italicizing",
+  "itemise": "itemize",
+  "itemised": "itemized",
+  "itemises": "itemizes",
+  "itemising": "itemizing",
+  "jeopardise": "jeopardize",
+  "jeopardised": "jeopardized",
+  "jeopardises": "jeopardizes",
+  "jeopardising": "jeopardizing",
+  "jewelled": "jeweled",
+  "jeweller": "jeweler",
+  "jewellers": "jewelers",
+  "jewellery": "jewelry",
+  "judgement": "judgment",
+  "kilogramme": "kilogram",
+  "kilogrammes": "kilograms",
+  "kilometre": "kilometer",
+  "kilometres": "kilometers",
+  "labelled": "labeled",
+  "labelling": "labeling",
+  "labour": "labor",
+  "laboured": "labored",
+  "labourer": "laborer",
+  "labourers": "laborers",
+  "labouring": "laboring",
+  "labours": "labors",
+  "lacklustre": "lackluster",
+  "legalisation": "legalization",
+  "legalise": "legalize",
+  "legalised": "legalized",
+  "legalises": "legalizes",
+  "legalising": "legalizing",
+  "legitimise": "legitimize",
+  "legitimised": "legitimized",
+  "legitimises": "legitimizes",
+  "legitimising": "legitimizing",
+  "leukaemia": "leukemia",
+  "levelled": "leveled",
+  "leveller": "leveler",
+  "levellers": "levelers",
+  "levelling": "leveling",
+  "libelled": "libeled",
+  "libelling": "libeling",
+  "libellous": "libelous",
+  "liberalisation": "liberalization",
+  "liberalise": "liberalize",
+  "liberalised": "liberalized",
+  "liberalises": "liberalizes",
+  "liberalising": "liberalizing",
+  "licence": "license",
+  "licenced": "licensed",
+  "licences": "licenses",
+  "licencing": "licensing",
+  "likeable": "likable",
+  "lionisation": "lionization",
+  "lionise": "lionize",
+  "lionised": "lionized",
+  "lionises": "lionizes",
+  "lionising": "lionizing",
+  "liquidise": "liquidize",
+  "liquidised": "liquidized",
+  "liquidiser": "liquidizer",
+  "liquidisers": "liquidizers",
+  "liquidises": "liquidizes",
+  "liquidising": "liquidizing",
+  "litre": "liter",
+  "litres": "liters",
+  "localise": "localize",
+  "localised": "localized",
+  "localises": "localizes",
+  "localising": "localizing",
+  "louvre": "louver",
+  "louvred": "louvered",
+  "louvres": "louvers",
+  "lustre": "luster",
+  "magnetise": "magnetize",
+  "magnetised": "magnetized",
+  "magnetises": "magnetizes",
+  "magnetising": "magnetizing",
+  "manoeuvrability": "maneuverability",
+  "manoeuvrable": "maneuverable",
+  "manoeuvre": "maneuver",
+  "manoeuvred": "maneuvered",
+  "manoeuvres": "maneuvers",
+  "manoeuvring": "maneuvering",
+  "manoeuvrings": "maneuverings",
+  "marginalisation": "marginalization",
+  "marginalise": "marginalize",
+  "marginalised": "marginalized",
+  "marginalises": "marginalizes",
+  "marginalising": "marginalizing",
+  "marshalled": "marshaled",
+  "marshalling": "marshaling",
+  "marvelled": "marveled",
+  "marvelling": "marveling",
+  "marvellous": "marvelous",
+  "marvellously": "marvelously",
+  "materialisation": "materialization",
+  "materialise": "materialize",
+  "materialised": "materialized",
+  "materialises": "materializes",
+  "materialising": "materializing",
+  "maximisation": "maximization",
+  "maximise": "maximize",
+  "maximised": "maximized",
+  "maximises": "maximizes",
+  "maximising": "maximizing",
+  "meagre": "meager",
+  "mechanisation": "mechanization",
+  "mechanise": "mechanize",
+  "mechanised": "mechanized",
+  "mechanises": "mechanizes",
+  "mechanising": "mechanizing",
+  "mediaeval": "medieval",
+  "memorialise": "memorialize",
+  "memorialised": "memorialized",
+  "memorialises": "memorializes",
+  "memorialising": "memorializing",
+  "memorise": "memorize",
+  "memorised": "memorized",
+  "memorises": "memorizes",
+  "memorising": "memorizing",
+  "mesmerise": "mesmerize",
+  "mesmerised": "mesmerized",
+  "mesmerises": "mesmerizes",
+  "mesmerising": "mesmerizing",
+  "metabolise": "metabolize",
+  "metabolised": "metabolized",
+  "metabolises": "metabolizes",
+  "metabolising": "metabolizing",
+  "metre": "meter",
+  "metres": "meters",
+  "mhm": "hmm",
+  "micrometre": "micrometer",
+  "micrometres": "micrometers",
+  "militarise": "militarize",
+  "militarised": "militarized",
+  "militarises": "militarizes",
+  "militarising": "militarizing",
+  "milligramme": "milligram",
+  "milligrammes": "milligrams",
+  "millilitre": "milliliter",
+  "millilitres": "milliliters",
+  "millimetre": "millimeter",
+  "millimetres": "millimeters",
+  "miniaturisation": "miniaturization",
+  "miniaturise": "miniaturize",
+  "miniaturised": "miniaturized",
+  "miniaturises": "miniaturizes",
+  "miniaturising": "miniaturizing",
+  "minibusses": "minibuses",
+  "minimise": "minimize",
+  "minimised": "minimized",
+  "minimises": "minimizes",
+  "minimising": "minimizing",
+  "misbehaviour": "misbehavior",
+  "misdemeanour": "misdemeanor",
+  "misdemeanours": "misdemeanors",
+  "misspelt": "misspelled",
+  "mitre": "miter",
+  "mitres": "miters",
+  "mm": "hmm",
+  "mmm": "hmm",
+  "mobilisation": "mobilization",
+  "mobilise": "mobilize",
+  "mobilised": "mobilized",
+  "mobilises": "mobilizes",
+  "mobilising": "mobilizing",
+  "modelled": "modeled",
+  "modeller": "modeler",
+  "modellers": "modelers",
+  "modelling": "modeling",
+  "modernise": "modernize",
+  "modernised": "modernized",
+  "modernises": "modernizes",
+  "modernising": "modernizing",
+  "moisturise": "moisturize",
+  "moisturised": "moisturized",
+  "moisturiser": "moisturizer",
+  "moisturisers": "moisturizers",
+  "moisturises": "moisturizes",
+  "moisturising": "moisturizing",
+  "monologue": "monolog",
+  "monologues": "monologs",
+  "monopolisation": "monopolization",
+  "monopolise": "monopolize",
+  "monopolised": "monopolized",
+  "monopolises": "monopolizes",
+  "monopolising": "monopolizing",
+  "moralise": "moralize",
+  "moralised": "moralized",
+  "moralises": "moralizes",
+  "moralising": "moralizing",
+  "motorised": "motorized",
+  "mould": "mold",
+  "moulded": "molded",
+  "moulder": "molder",
+  "mouldered": "moldered",
+  "mouldering": "moldering",
+  "moulders": "molders",
+  "mouldier": "moldier",
+  "mouldiest": "moldiest",
+  "moulding": "molding",
+  "mouldings": "moldings",
+  "moulds": "molds",
+  "mouldy": "moldy",
+  "moult": "molt",
+  "moulted": "molted",
+  "moulting": "molting",
+  "moults": "molts",
+  "moustache": "mustache",
+  "moustached": "mustached",
+  "moustaches": "mustaches",
+  "moustachioed": "mustachioed",
+  "multicoloured": "multicolored",
+  "nationalisation": "nationalization",
+  "nationalisations": "nationalizations",
+  "nationalise": "nationalize",
+  "nationalised": "nationalized",
+  "nationalises": "nationalizes",
+  "nationalising": "nationalizing",
+  "naturalisation": "naturalization",
+  "naturalise": "naturalize",
+  "naturalised": "naturalized",
+  "naturalises": "naturalizes",
+  "naturalising": "naturalizing",
+  "neighbour": "neighbor",
+  "neighbourhood": "neighborhood",
+  "neighbourhoods": "neighborhoods",
+  "neighbouring": "neighboring",
+  "neighbourliness": "neighborliness",
+  "neighbourly": "neighborly",
+  "neighbours": "neighbors",
+  "neutralisation": "neutralization",
+  "neutralise": "neutralize",
+  "neutralised": "neutralized",
+  "neutralises": "neutralizes",
+  "neutralising": "neutralizing",
+  "normalisation": "normalization",
+  "normalise": "normalize",
+  "normalised": "normalized",
+  "normalises": "normalizes",
+  "normalising": "normalizing",
+  "odour": "odor",
+  "odourless": "odorless",
+  "odours": "odors",
+  "oesophagus": "esophagus",
+  "oesophaguses": "esophaguses",
+  "oestrogen": "estrogen",
+  "offence": "offense",
+  "offences": "offenses",
+  "omelette": "omelet",
+  "omelettes": "omelets",
+  "optimise": "optimize",
+  "optimised": "optimized",
+  "optimises": "optimizes",
+  "optimising": "optimizing",
+  "organisation": "organization",
+  "organisational": "organizational",
+  "organisations": "organizations",
+  "organise": "organize",
+  "organised": "organized",
+  "organiser": "organizer",
+  "organisers": "organizers",
+  "organises": "organizes",
+  "organising": "organizing",
+  "orthopaedic": "orthopedic",
+  "orthopaedics": "orthopedics",
+  "ostracise": "ostracize",
+  "ostracised": "ostracized",
+  "ostracises": "ostracizes",
+  "ostracising": "ostracizing",
+  "outmanoeuvre": "outmaneuver",
+  "outmanoeuvred": "outmaneuvered",
+  "outmanoeuvres": "outmaneuvers",
+  "outmanoeuvring": "outmaneuvering",
+  "overemphasise": "overemphasize",
+  "overemphasised": "overemphasized",
+  "overemphasises": "overemphasizes",
+  "overemphasising": "overemphasizing",
+  "oxidisation": "oxidization",
+  "oxidise": "oxidize",
+  "oxidised": "oxidized",
+  "oxidises": "oxidizes",
+  "oxidising": "oxidizing",
+  "paederast": "pederast",
+  "paederasts": "pederasts",
+  "paediatric": "pediatric",
+  "paediatrician": "pediatrician",
+  "paediatricians": "pediatricians",
+  "paediatrics": "pediatrics",
+  "paedophile": "pedophile",
+  "paedophiles": "pedophiles",
+  "paedophilia": "pedophilia",
+  "palaeolithic": "paleolithic",
+  "palaeontologist": "paleontologist",
+  "palaeontologists": "paleontologists",
+  "palaeontology": "paleontology",
+  "panelled": "paneled",
+  "panelling": "paneling",
+  "panellist": "panelist",
+  "panellists": "panelists",
+  "paralyse": "paralyze",
+  "paralysed": "paralyzed",
+  "paralyses": "paralyzes",
+  "paralysing": "paralyzing",
+  "parcelled": "parceled",
+  "parcelling": "parceling",
+  "parlour": "parlor",
+  "parlours": "parlors",
+  "particularise": "particularize",
+  "particularised": "particularized",
+  "particularises": "particularizes",
+  "particularising": "particularizing",
+  "passivisation": "passivization",
+  "passivise": "passivize",
+  "passivised": "passivized",
+  "passivises": "passivizes",
+  "passivising": "passivizing",
+  "pasteurisation": "pasteurization",
+  "pasteurise": "pasteurize",
+  "pasteurised": "pasteurized",
+  "pasteurises": "pasteurizes",
+  "pasteurising": "pasteurizing",
+  "patronise": "patronize",
+  "patronised": "patronized",
+  "patronises": "patronizes",
+  "patronising": "patronizing",
+  "patronisingly": "patronizingly",
+  "pedalled": "pedaled",
+  "pedalling": "pedaling",
+  "pedestrianisation": "pedestrianization",
+  "pedestrianise": "pedestrianize",
+  "pedestrianised": "pedestrianized",
+  "pedestrianises": "pedestrianizes",
+  "pedestrianising": "pedestrianizing",
+  "penalise": "penalize",
+  "penalised": "penalized",
+  "penalises": "penalizes",
+  "penalising": "penalizing",
+  "pencilled": "penciled",
+  "pencilling": "penciling",
+  "personalise": "personalize",
+  "personalised": "personalized",
+  "personalises": "personalizes",
+  "personalising": "personalizing",
+  "pharmacopoeia": "pharmacopeia",
+  "pharmacopoeias": "pharmacopeias",
+  "philosophise": "philosophize",
+  "philosophised": "philosophized",
+  "philosophises": "philosophizes",
+  "philosophising": "philosophizing",
+  "philtre": "filter",
+  "philtres": "filters",
+  "phoney": "phony",
+  "plagiarise": "plagiarize",
+  "plagiarised": "plagiarized",
+  "plagiarises": "plagiarizes",
+  "plagiarising": "plagiarizing",
+  "plough": "plow",
+  "ploughed": "plowed",
+  "ploughing": "plowing",
+  "ploughman": "plowman",
+  "ploughmen": "plowmen",
+  "ploughs": "plows",
+  "ploughshare": "plowshare",
+  "ploughshares": "plowshares",
+  "polarisation": "polarization",
+  "polarise": "polarize",
+  "polarised": "polarized",
+  "polarises": "polarizes",
+  "polarising": "polarizing",
+  "politicisation": "politicization",
+  "politicise": "politicize",
+  "politicised": "politicized",
+  "politicises": "politicizes",
+  "politicising": "politicizing",
+  "popularisation": "popularization",
+  "popularise": "popularize",
+  "popularised": "popularized",
+  "popularises": "popularizes",
+  "popularising": "popularizing",
+  "pouffe": "pouf",
+  "pouffes": "poufs",
+  "practise": "practice",
+  "practised": "practiced",
+  "practises": "practices",
+  "practising": "practicing",
+  "praesidium": "presidium",
+  "praesidiums": "presidiums",
+  "pressurisation": "pressurization",
+  "pressurise": "pressurize",
+  "pressurised": "pressurized",
+  "pressurises": "pressurizes",
+  "pressurising": "pressurizing",
+  "pretence": "pretense",
+  "pretences": "pretenses",
+  "primaeval": "primeval",
+  "prioritisation": "prioritization",
+  "prioritise": "prioritize",
+  "prioritised": "prioritized",
+  "prioritises": "prioritizes",
+  "prioritising": "prioritizing",
+  "privatisation": "privatization",
+  "privatisations": "privatizations",
+  "privatise": "privatize",
+  "privatised": "privatized",
+  "privatises": "privatizes",
+  "privatising": "privatizing",
+  "professionalisation": "professionalization",
+  "professionalise": "professionalize",
+  "professionalised": "professionalized",
+  "professionalises": "professionalizes",
+  "professionalising": "professionalizing",
+  "programme": "program",
+  "programmes": "programs",
+  "prologue": "prolog",
+  "prologues": "prologs",
+  "propagandise": "propagandize",
+  "propagandised": "propagandized",
+  "propagandises": "propagandizes",
+  "propagandising": "propagandizing",
+  "proselytise": "proselytize",
+  "proselytised": "proselytized",
+  "proselytiser": "proselytizer",
+  "proselytisers": "proselytizers",
+  "proselytises": "proselytizes",
+  "proselytising": "proselytizing",
+  "psychoanalyse": "psychoanalyze",
+  "psychoanalysed": "psychoanalyzed",
+  "psychoanalyses": "psychoanalyzes",
+  "psychoanalysing": "psychoanalyzing",
+  "publicise": "publicize",
+  "publicised": "publicized",
+  "publicises": "publicizes",
+  "publicising": "publicizing",
+  "pulverisation": "pulverization",
+  "pulverise": "pulverize",
+  "pulverised": "pulverized",
+  "pulverises": "pulverizes",
+  "pulverising": "pulverizing",
+  "pummelled": "pummel",
+  "pummelling": "pummeled",
+  "pyjama": "pajama",
+  "pyjamas": "pajamas",
+  "pzazz": "pizzazz",
+  "quarrelled": "quarreled",
+  "quarrelling": "quarreling",
+  "radicalise": "radicalize",
+  "radicalised": "radicalized",
+  "radicalises": "radicalizes",
+  "radicalising": "radicalizing",
+  "rancour": "rancor",
+  "randomise": "randomize",
+  "randomised": "randomized",
+  "randomises": "randomizes",
+  "randomising": "randomizing",
+  "rationalisation": "rationalization",
+  "rationalisations": "rationalizations",
+  "rationalise": "rationalize",
+  "rationalised": "rationalized",
+  "rationalises": "rationalizes",
+  "rationalising": "rationalizing",
+  "ravelled": "raveled",
+  "ravelling": "raveling",
+  "realisable": "realizable",
+  "realisation": "realization",
+  "realisations": "realizations",
+  "realise": "realize",
+  "realised": "realized",
+  "realises": "realizes",
+  "realising": "realizing",
+  "recognisable": "recognizable",
+  "recognisably": "recognizably",
+  "recognisance": "recognizance",
+  "recognise": "recognize",
+  "recognised": "recognized",
+  "recognises": "recognizes",
+  "recognising": "recognizing",
+  "reconnoitre": "reconnoiter",
+  "reconnoitred": "reconnoitered",
+  "reconnoitres": "reconnoiters",
+  "reconnoitring": "reconnoitering",
+  "refuelled": "refueled",
+  "refuelling": "refueling",
+  "regularisation": "regularization",
+  "regularise": "regularize",
+  "regularised": "regularized",
+  "regularises": "regularizes",
+  "regularising": "regularizing",
+  "remodelled": "remodeled",
+  "remodelling": "remodeling",
+  "remould": "remold",
+  "remoulded": "remolded",
+  "remoulding": "remolding",
+  "remoulds": "remolds",
+  "reorganisation": "reorganization",
+  "reorganisations": "reorganizations",
+  "reorganise": "reorganize",
+  "reorganised": "reorganized",
+  "reorganises": "reorganizes",
+  "reorganising": "reorganizing",
+  "revelled": "reveled",
+  "reveller": "reveler",
+  "revellers": "revelers",
+  "revelling": "reveling",
+  "revitalise": "revitalize",
+  "revitalised": "revitalized",
+  "revitalises": "revitalizes",
+  "revitalising": "revitalizing",
+  "revolutionise": "revolutionize",
+  "revolutionised": "revolutionized",
+  "revolutionises": "revolutionizes",
+  "revolutionising": "revolutionizing",
+  "rhapsodise": "rhapsodize",
+  "rhapsodised": "rhapsodized",
+  "rhapsodises": "rhapsodizes",
+  "rhapsodising": "rhapsodizing",
+  "rigour": "rigor",
+  "rigours": "rigors",
+  "ritualised": "ritualized",
+  "rivalled": "rivaled",
+  "rivalling": "rivaling",
+  "romanticise": "romanticize",
+  "romanticised": "romanticized",
+  "romanticises": "romanticizes",
+  "romanticising": "romanticizing",
+  "rumour": "rumor",
+  "rumoured": "rumored",
+  "rumours": "rumors",
+  "sabre": "saber",
+  "sabres": "sabers",
+  "saltpetre": "saltpeter",
+  "sanitise": "sanitize",
+  "sanitised": "sanitized",
+  "sanitises": "sanitizes",
+  "sanitising": "sanitizing",
+  "satirise": "satirize",
+  "satirised": "satirized",
+  "satirises": "satirizes",
+  "satirising": "satirizing",
+  "saviour": "savior",
+  "saviours": "saviors",
+  "savour": "savor",
+  "savoured": "savored",
+  "savouries": "savories",
+  "savouring": "savoring",
+  "savours": "savors",
+  "savoury": "savory",
+  "scandalise": "scandalize",
+  "scandalised": "scandalized",
+  "scandalises": "scandalizes",
+  "scandalising": "scandalizing",
+  "sceptic": "skeptic",
+  "sceptical": "skeptical",
+  "sceptically": "skeptically",
+  "scepticism": "skepticism",
+  "sceptics": "skeptics",
+  "sceptre": "scepter",
+  "sceptres": "scepters",
+  "scrutinise": "scrutinize",
+  "scrutinised": "scrutinized",
+  "scrutinises": "scrutinizes",
+  "scrutinising": "scrutinizing",
+  "secularisation": "secularization",
+  "secularise": "secularize",
+  "secularised": "secularized",
+  "secularises": "secularizes",
+  "secularising": "secularizing",
+  "sensationalise": "sensationalize",
+  "sensationalised": "sensationalized",
+  "sensationalises": "sensationalizes",
+  "sensationalising": "sensationalizing",
+  "sensitise": "sensitize",
+  "sensitised": "sensitized",
+  "sensitises": "sensitizes",
+  "sensitising": "sensitizing",
+  "sentimentalise": "sentimentalize",
+  "sentimentalised": "sentimentalized",
+  "sentimentalises": "sentimentalizes",
+  "sentimentalising": "sentimentalizing",
+  "sepulchre": "sepulcher",
+  "sepulchres": "sepulchers",
+  "serialisation": "serialization",
+  "serialisations": "serializations",
+  "serialise": "serialize",
+  "serialised": "serialized",
+  "serialises": "serializes",
+  "serialising": "serializing",
+  "sermonise": "sermonize",
+  "sermonised": "sermonized",
+  "sermonises": "sermonizes",
+  "sermonising": "sermonizing",
+  "sheikh": "sheik",
+  "shovelled": "shoveled",
+  "shovelling": "shoveling",
+  "shrivelled": "shriveled",
+  "shrivelling": "shriveling",
+  "signalise": "signalize",
+  "signalised": "signalized",
+  "signalises": "signalizes",
+  "signalising": "signalizing",
+  "signalled": "signaled",
+  "signalling": "signaling",
+  "smoulder": "smolder",
+  "smouldered": "smoldered",
+  "smouldering": "smoldering",
+  "smoulders": "smolders",
+  "snivelled": "sniveled",
+  "snivelling": "sniveling",
+  "snorkelled": "snorkeled",
+  "snorkelling": "snorkeling",
+  "snowplough": "snowplow",
+  "snowploughs": "snowplow",
+  "socialisation": "socialization",
+  "socialise": "socialize",
+  "socialised": "socialized",
+  "socialises": "socializes",
+  "socialising": "socializing",
+  "sodomise": "sodomize",
+  "sodomised": "sodomized",
+  "sodomises": "sodomizes",
+  "sodomising": "sodomizing",
+  "solemnise": "solemnize",
+  "solemnised": "solemnized",
+  "solemnises": "solemnizes",
+  "solemnising": "solemnizing",
+  "sombre": "somber",
+  "specialisation": "specialization",
+  "specialisations": "specializations",
+  "specialise": "specialize",
+  "specialised": "specialized",
+  "specialises": "specializes",
+  "specialising": "specializing",
+  "spectre": "specter",
+  "spectres": "specters",
+  "spiralled": "spiraled",
+  "spiralling": "spiraling",
+  "splendour": "splendor",
+  "splendours": "splendors",
+  "squirrelled": "squirreled",
+  "squirrelling": "squirreling",
+  "stabilisation": "stabilization",
+  "stabilise": "stabilize",
+  "stabilised": "stabilized",
+  "stabiliser": "stabilizer",
+  "stabilisers": "stabilizers",
+  "stabilises": "stabilizes",
+  "stabilising": "stabilizing",
+  "standardisation": "standardization",
+  "standardise": "standardize",
+  "standardised": "standardized",
+  "standardises": "standardizes",
+  "standardising": "standardizing",
+  "stencilled": "stenciled",
+  "stencilling": "stenciling",
+  "sterilisation": "sterilization",
+  "sterilisations": "sterilizations",
+  "sterilise": "sterilize",
+  "sterilised": "sterilized",
+  "steriliser": "sterilizer",
+  "sterilisers": "sterilizers",
+  "sterilises": "sterilizes",
+  "sterilising": "sterilizing",
+  "stigmatisation": "stigmatization",
+  "stigmatise": "stigmatize",
+  "stigmatised": "stigmatized",
+  "stigmatises": "stigmatizes",
+  "stigmatising": "stigmatizing",
+  "storey": "story",
+  "storeys": "stories",
+  "subsidisation": "subsidization",
+  "subsidise": "subsidize",
+  "subsidised": "subsidized",
+  "subsidiser": "subsidizer",
+  "subsidisers": "subsidizers",
+  "subsidises": "subsidizes",
+  "subsidising": "subsidizing",
+  "succour": "succor",
+  "succoured": "succored",
+  "succouring": "succoring",
+  "succours": "succors",
+  "sulphate": "sulfate",
+  "sulphates": "sulfates",
+  "sulphide": "sulfide",
+  "sulphides": "sulfides",
+  "sulphur": "sulfur",
+  "sulphurous": "sulfurous",
+  "summarise": "summarize",
+  "summarised": "summarized",
+  "summarises": "summarizes",
+  "summarising": "summarizing",
+  "swivelled": "swiveled",
+  "swivelling": "swiveling",
+  "symbolise": "symbolize",
+  "symbolised": "symbolized",
+  "symbolises": "symbolizes",
+  "symbolising": "symbolizing",
+  "sympathise": "sympathize",
+  "sympathised": "sympathized",
+  "sympathiser": "sympathizer",
+  "sympathisers": "sympathizers",
+  "sympathises": "sympathizes",
+  "sympathising": "sympathizing",
+  "synchronisation": "synchronization",
+  "synchronise": "synchronize",
+  "synchronised": "synchronized",
+  "synchronises": "synchronizes",
+  "synchronising": "synchronizing",
+  "synthesise": "synthesize",
+  "synthesised": "synthesized",
+  "synthesiser": "synthesizer",
+  "synthesisers": "synthesizers",
+  "synthesises": "synthesizes",
+  "synthesising": "synthesizing",
+  "syphon": "siphon",
+  "syphoned": "siphoned",
+  "syphoning": "siphoning",
+  "syphons": "siphons",
+  "systematisation": "systematization",
+  "systematise": "systematize",
+  "systematised": "systematized",
+  "systematises": "systematizes",
+  "systematising": "systematizing",
+  "tantalise": "tantalize",
+  "tantalised": "tantalized",
+  "tantalises": "tantalizes",
+  "tantalising": "tantalizing",
+  "tantalisingly": "tantalizingly",
+  "tasselled": "tasseled",
+  "technicolour": "technicolor",
+  "temporise": "temporize",
+  "temporised": "temporized",
+  "temporises": "temporizes",
+  "temporising": "temporizing",
+  "tenderise": "tenderize",
+  "tenderised": "tenderized",
+  "tenderises": "tenderizes",
+  "tenderising": "tenderizing",
+  "terrorise": "terrorize",
+  "terrorised": "terrorized",
+  "terrorises": "terrorizes",
+  "terrorising": "terrorizing",
+  "theatre": "theater",
+  "theatregoer": "theatergoer",
+  "theatregoers": "theatergoers",
+  "theatres": "theaters",
+  "theorise": "theorize",
+  "theorised": "theorized",
+  "theorises": "theorizes",
+  "theorising": "theorizing",
+  "tonne": "ton",
+  "tonnes": "tons",
+  "towelled": "toweled",
+  "towelling": "toweling",
+  "toxaemia": "toxemia",
+  "tranquillise": "tranquilize",
+  "tranquillised": "tranquilized",
+  "tranquilliser": "tranquilizer",
+  "tranquillisers": "tranquilizers",
+  "tranquillises": "tranquilizes",
+  "tranquillising": "tranquilizing",
+  "tranquillity": "tranquility",
+  "tranquillize": "tranquilize",
+  "tranquillized": "tranquilized",
+  "tranquillizer": "tranquilizer",
+  "tranquillizers": "tranquilizers",
+  "tranquillizes": "tranquilizes",
+  "tranquillizing": "tranquilizing",
+  "tranquilly": "tranquility",
+  "transistorised": "transistorized",
+  "traumatise": "traumatize",
+  "traumatised": "traumatized",
+  "traumatises": "traumatizes",
+  "traumatising": "traumatizing",
+  "travelled": "traveled",
+  "traveller": "traveler",
+  "travellers": "travelers",
+  "travelling": "traveling",
+  "travelog": "travelogue",
+  "travelogs": "travelogues",
+  "trialled": "trialed",
+  "trialling": "trialing",
+  "tricolour": "tricolor",
+  "tricolours": "tricolors",
+  "trivialise": "trivialize",
+  "trivialised": "trivialized",
+  "trivialises": "trivializes",
+  "trivialising": "trivializing",
+  "tumour": "tumor",
+  "tumours": "tumors",
+  "tunnelled": "tunneled",
+  "tunnelling": "tunneling",
+  "tyrannise": "tyrannize",
+  "tyrannised": "tyrannized",
+  "tyrannises": "tyrannizes",
+  "tyrannising": "tyrannizing",
+  "tyre": "tire",
+  "tyres": "tires",
+  "unauthorised": "unauthorized",
+  "uncivilised": "uncivilized",
+  "underutilised": "underutilized",
+  "unequalled": "unequaled",
+  "unfavourable": "unfavorable",
+  "unfavourably": "unfavorably",
+  "unionisation": "unionization",
+  "unionise": "unionize",
+  "unionised": "unionized",
+  "unionises": "unionizes",
+  "unionising": "unionizing",
+  "unorganised": "unorganized",
+  "unravelled": "unraveled",
+  "unravelling": "unraveling",
+  "unrecognisable": "unrecognizable",
+  "unrecognised": "unrecognized",
+  "unrivalled": "unrivaled",
+  "unsavoury": "unsavory",
+  "untrammelled": "untrammeled",
+  "urbanisation": "urbanization",
+  "urbanise": "urbanize",
+  "urbanised": "urbanized",
+  "urbanises": "urbanizes",
+  "urbanising": "urbanizing",
+  "utilisable": "utilizable",
+  "utilisation": "utilization",
+  "utilise": "utilize",
+  "utilised": "utilized",
+  "utilises": "utilizes",
+  "utilising": "utilizing",
+  "valour": "valor",
+  "vandalise": "vandalize",
+  "vandalised": "vandalized",
+  "vandalises": "vandalizes",
+  "vandalising": "vandalizing",
+  "vaporisation": "vaporization",
+  "vaporise": "vaporize",
+  "vaporised": "vaporized",
+  "vaporises": "vaporizes",
+  "vaporising": "vaporizing",
+  "vapour": "vapor",
+  "vapours": "vapors",
+  "verbalise": "verbalize",
+  "verbalised": "verbalized",
+  "verbalises": "verbalizes",
+  "verbalising": "verbalizing",
+  "victimisation": "victimization",
+  "victimise": "victimize",
+  "victimised": "victimized",
+  "victimises": "victimizes",
+  "victimising": "victimizing",
+  "videodisc": "videodisk",
+  "videodiscs": "videodisks",
+  "vigour": "vigor",
+  "visualisation": "visualization",
+  "visualisations": "visualizations",
+  "visualise": "visualize",
+  "visualised": "visualized",
+  "visualises": "visualizes",
+  "visualising": "visualizing",
+  "vocalisation": "vocalization",
+  "vocalisations": "vocalizations",
+  "vocalise": "vocalize",
+  "vocalised": "vocalized",
+  "vocalises": "vocalizes",
+  "vocalising": "vocalizing",
+  "vulcanised": "vulcanized",
+  "vulgarisation": "vulgarization",
+  "vulgarise": "vulgarize",
+  "vulgarised": "vulgarized",
+  "vulgarises": "vulgarizes",
+  "vulgarising": "vulgarizing",
+  "waggon": "wagon",
+  "waggons": "wagons",
+  "watercolour": "watercolor",
+  "watercolours": "watercolors",
+  "weaselled": "weaseled",
+  "weaselling": "weaseling",
+  "westernisation": "westernization",
+  "westernise": "westernize",
+  "westernised": "westernized",
+  "westernises": "westernizes",
+  "westernising": "westernizing",
+  "womanise": "womanize",
+  "womanised": "womanized",
+  "womaniser": "womanizer",
+  "womanisers": "womanizers",
+  "womanises": "womanizes",
+  "womanising": "womanizing",
+  "woollen": "woolen",
+  "woollens": "woolens",
+  "woollies": "woolies",
+  "woolly": "wooly",
+  "worshipped": "worshiped",
+  "worshipper": "worshiper",
+  "worshipping": "worshiping",
+  "yodelled": "yodeled",
+  "yodelling": "yodeling",
+  "yoghourt": "yogurt",
+  "yoghourts": "yogurts",
+  "yoghurt": "yogurt",
+  "yoghurts": "yogurts"
+}
diff --git a/whisper-fine-tuning-event/preprocessor_config.json b/whisper-fine-tuning-event/preprocessor_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..91876762a536a746d268353c5cba57286e76b058
--- /dev/null
+++ b/whisper-fine-tuning-event/preprocessor_config.json
@@ -0,0 +1,14 @@
+{
+  "chunk_length": 30,
+  "feature_extractor_type": "WhisperFeatureExtractor",
+  "feature_size": 80,
+  "hop_length": 160,
+  "n_fft": 400,
+  "n_samples": 480000,
+  "nb_max_frames": 3000,
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "processor_class": "WhisperProcessor",
+  "return_attention_mask": false,
+  "sampling_rate": 16000
+}
diff --git a/whisper-fine-tuning-event/requirements.txt b/whisper-fine-tuning-event/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..03679c94799369ac623d3329a8351dac10b8bdb3
--- /dev/null
+++ b/whisper-fine-tuning-event/requirements.txt
@@ -0,0 +1,9 @@
+torch>=1.7
+torchaudio
+git+https://github.com/huggingface/transformers
+git+https://github.com/huggingface/datasets
+librosa
+jiwer
+evaluate>=0.3.0
+more-itertools
+tensorboard
diff --git a/whisper-fine-tuning-event/run_eval_whisper_streaming.py b/whisper-fine-tuning-event/run_eval_whisper_streaming.py
new file mode 100644
index 0000000000000000000000000000000000000000..48806d39c4525f73113a317ed9af7802f10a66a8
--- /dev/null
+++ b/whisper-fine-tuning-event/run_eval_whisper_streaming.py
@@ -0,0 +1,162 @@
+import argparse
+
+from transformers import pipeline
+from transformers.models.whisper.english_normalizer import BasicTextNormalizer
+from datasets import load_dataset, Audio
+import evaluate
+
+wer_metric = evaluate.load("wer")
+
+
+def is_target_text_in_range(ref):
+    if ref.strip() == "ignore time segment in scoring":
+        return False
+    else:
+        return ref.strip() != ""
+
+
+def get_text(sample):
+    if "text" in sample:
+        return sample["text"]
+    elif "sentence" in sample:
+        return sample["sentence"]
+    elif "normalized_text" in sample:
+        return sample["normalized_text"]
+    elif "transcript" in sample:
+        return sample["transcript"]
+    elif "transcription" in sample:
+        return sample["transcription"]
+    else:
+        raise ValueError(
+            f"Expected transcript column of either 'text', 'sentence', 'normalized_text' or 'transcript'. Got sample of "
+            ".join{sample.keys()}. Ensure a text column name is present in the dataset."
+        )
+
+
+whisper_norm = BasicTextNormalizer()
+
+
+def normalise(batch):
+    batch["norm_text"] = whisper_norm(get_text(batch))
+    return batch
+
+
+def data(dataset):
+    for i, item in enumerate(dataset):
+        yield {**item["audio"], "reference": item["norm_text"]}
+
+
+def main(args):
+    batch_size = args.batch_size
+    whisper_asr = pipeline(
+        "automatic-speech-recognition", model=args.model_id, device=args.device
+    )
+
+    whisper_asr.model.config.forced_decoder_ids = (
+        whisper_asr.tokenizer.get_decoder_prompt_ids(
+            language=args.language, task="transcribe"
+        )
+    )
+
+    dataset = load_dataset(
+        args.dataset,
+        args.config,
+        split=args.split,
+        streaming=args.streaming,
+        use_auth_token=True,
+    )
+
+    # Only uncomment for debugging
+    dataset = dataset.take(args.max_eval_samples)
+
+    dataset = dataset.cast_column("audio", Audio(sampling_rate=16000))
+    dataset = dataset.map(normalise)
+    dataset = dataset.filter(is_target_text_in_range, input_columns=["norm_text"])
+
+    predictions = []
+    references = []
+
+    # run streamed inference
+    for out in whisper_asr(data(dataset), batch_size=batch_size):
+        predictions.append(whisper_norm(out["text"]))
+        references.append(out["reference"][0])
+
+    wer = wer_metric.compute(references=references, predictions=predictions)
+    wer = round(100 * wer, 2)
+
+    print("WER:", wer)
+    evaluate.push_to_hub(
+        model_id=args.model_id,
+        metric_value=wer,
+        metric_type="wer",
+        metric_name="WER",
+        dataset_name=args.dataset,
+        dataset_type=args.dataset,
+        dataset_split=args.split,
+        dataset_config=args.config,
+        task_type="automatic-speech-recognition",
+        task_name="Automatic Speech Recognition"
+    )
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--model_id",
+        type=str,
+        required=True,
+        help="Model identifier. Should be loadable with 🤗 Transformers",
+    )
+    parser.add_argument(
+        "--dataset",
+        type=str,
+        default="mozilla-foundation/common_voice_11_0",
+        help="Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets",
+    )
+    parser.add_argument(
+        "--config",
+        type=str,
+        required=True,
+        help="Config of the dataset. *E.g.* `'en'` for the English split of Common Voice",
+    )
+    parser.add_argument(
+        "--split",
+        type=str,
+        default="test",
+        help="Split of the dataset. *E.g.* `'test'`",
+    )
+
+    parser.add_argument(
+        "--device",
+        type=int,
+        default=-1,
+        help="The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.",
+    )
+    parser.add_argument(
+        "--batch_size",
+        type=int,
+        default=16,
+        help="Number of samples to go through each streamed batch.",
+    )
+    parser.add_argument(
+        "--max_eval_samples",
+        type=int,
+        default=None,
+        help="Number of samples to be evaluated. Put a lower number e.g. 64 for testing this script.",
+    )
+    parser.add_argument(
+        "--streaming",
+        type=bool,
+        default=True,
+        help="Choose whether you'd like to download the entire dataset or stream it during the evaluation.",
+    )
+    parser.add_argument(
+        "--language",
+        type=str,
+        required=True,
+        help="Two letter language code for the transcription language, e.g. use 'en' for English.",
+    )
+    args = parser.parse_args()
+
+    main(args)
diff --git a/whisper-fine-tuning-event/run_speech_recognition_seq2seq_streaming.py b/whisper-fine-tuning-event/run_speech_recognition_seq2seq_streaming.py
new file mode 100644
index 0000000000000000000000000000000000000000..abd0a80fdd6587494ed7d1e084cfa8e685b5ab2e
--- /dev/null
+++ b/whisper-fine-tuning-event/run_speech_recognition_seq2seq_streaming.py
@@ -0,0 +1,629 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2022 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Fine-tuning the library models for sequence to sequence speech recognition
+with 🤗 Datasets' streaming mode.
+"""
+# You can also adapt this script for your own sequence to sequence speech
+# recognition task. Pointers for this are left as comments.
+
+import logging
+import os
+import sys
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional, Union
+
+import datasets
+import torch
+from datasets import DatasetDict, IterableDatasetDict, interleave_datasets, load_dataset
+from torch.utils.data import IterableDataset
+
+import evaluate
+import transformers
+from transformers import (
+    AutoConfig,
+    AutoFeatureExtractor,
+    AutoModelForSpeechSeq2Seq,
+    AutoProcessor,
+    AutoTokenizer,
+    HfArgumentParser,
+    Seq2SeqTrainer,
+    Seq2SeqTrainingArguments,
+    TrainerCallback,
+    set_seed,
+)
+from transformers.models.whisper.english_normalizer import BasicTextNormalizer
+from transformers.trainer_pt_utils import IterableDatasetShard
+from transformers.trainer_utils import get_last_checkpoint, is_main_process
+from transformers.utils import check_min_version, send_example_telemetry
+from transformers.utils.versions import require_version
+
+
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.25.0.dev0")
+
+require_version("datasets>=1.18.2", "To fix: pip install -r examples/pytorch/speech-recognition/requirements.txt")
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
+    """
+
+    model_name_or_path: str = field(
+        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
+    )
+    config_name: Optional[str] = field(
+        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
+    )
+    tokenizer_name: Optional[str] = field(
+        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
+    )
+    feature_extractor_name: Optional[str] = field(
+        default=None, metadata={"help": "feature extractor name or path if not the same as model_name"}
+    )
+    cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"},
+    )
+    use_fast_tokenizer: bool = field(
+        default=True,
+        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
+    )
+    model_revision: str = field(
+        default="main",
+        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
+    )
+    use_auth_token: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
+                "with private models)."
+            )
+        },
+    )
+    freeze_feature_encoder: bool = field(
+        default=True, metadata={"help": "Whether to freeze the feature encoder layers of the model."}
+    )
+    freeze_encoder: bool = field(
+        default=False, metadata={"help": "Whether to freeze the entire encoder of the seq2seq model."}
+    )
+    forced_decoder_ids: List[List[int]] = field(
+        default=None,
+        metadata={
+            "help": (
+                "A list of pairs of integers which indicates a mapping from generation indices to token indices "
+                "that will be forced before sampling. For example, [[0, 123]] means the first generated token "
+                "will always be a token of index 123."
+            )
+        },
+    )
+    suppress_tokens: List[int] = field(
+        default=None, metadata={"help": "A list of tokens that will be suppressed at generation."}
+    )
+    model_index_name: str = field(default=None, metadata={"help": "Pretty name for the model card."})
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+    """
+
+    dataset_name: str = field(
+        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
+    )
+    dataset_config_name: Optional[str] = field(
+        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
+    )
+    text_column: Optional[str] = field(
+        default=None,
+        metadata={"help": "The name of the column in the datasets containing the full texts (for summarization)."},
+    )
+    max_train_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of training examples to this "
+                "value if set."
+            )
+        },
+    )
+    max_eval_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
+                "value if set."
+            )
+        },
+    )
+    audio_column_name: str = field(
+        default="audio",
+        metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},
+    )
+    text_column_name: str = field(
+        default="text",
+        metadata={"help": "The name of the dataset column containing the text data. Defaults to 'text'"},
+    )
+    max_duration_in_seconds: float = field(
+        default=20.0,
+        metadata={
+            "help": (
+                "Truncate audio files that are longer than `max_duration_in_seconds` seconds to"
+                " 'max_duration_in_seconds`"
+            )
+        },
+    )
+    min_duration_in_seconds: float = field(
+        default=0.0, metadata={"help": "Filter audio files that are shorter than `min_duration_in_seconds` seconds"}
+    )
+    train_split_name: str = field(
+        default="train",
+        metadata={
+            "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
+        },
+    )
+    eval_split_name: str = field(
+        default="test",
+        metadata={
+            "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
+        },
+    )
+    do_lower_case: bool = field(
+        default=False,
+        metadata={"help": "Whether the target text should be lower cased."},
+    )
+    do_remove_punctuation: bool = field(
+        default=False,
+        metadata={"help": "Whether the target text should be striped of punctuation."},
+    )
+    do_normalize_eval: bool = field(
+        default=True,
+        metadata={"help": "Whether to normalise the references and predictions in the eval WER calculation."},
+    )
+    language: str = field(
+        default=None,
+        metadata={
+            "help": (
+                "Language for multilingual fine-tuning. This argument should be set for multilingual fine-tuning "
+                "only. For English speech recognition, it should be set to `None`."
+            )
+        },
+    )
+    task: str = field(
+        default="transcribe",
+        metadata={"help": "Task, either `transcribe` for speech recognition or `translate` for speech translation."},
+    )
+    shuffle_buffer_size: Optional[int] = field(
+        default=500,
+        metadata={
+            "help": (
+                "The number of streamed examples to download before shuffling them. The large the buffer, "
+                "the closer it is to real offline shuffling."
+            )
+        },
+    )
+    streaming: bool = field(
+        default=True,
+        metadata={"help": "Whether to use streaming mode to load and pre-process the data."},
+    )
+
+
+@dataclass
+class DataCollatorSpeechSeq2SeqWithPadding:
+    """
+    Data collator that will dynamically pad the inputs received.
+    Args:
+        processor ([`WhisperProcessor`])
+            The processor used for processing the data.
+        decoder_start_token_id (`int`)
+            The begin-of-sentence of the decoder.
+    """
+
+    processor: Any
+    decoder_start_token_id: int
+
+    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
+        # split inputs and labels since they have to be of different lengths and need
+        # different padding methods
+        model_input_name = self.processor.model_input_names[0]
+        input_features = [{model_input_name: feature[model_input_name]} for feature in features]
+        label_features = [{"input_ids": feature["labels"]} for feature in features]
+
+        batch = self.processor.feature_extractor.pad(input_features, return_tensors="pt")
+
+        labels_batch = self.processor.tokenizer.pad(label_features, return_tensors="pt")
+
+        # replace padding with -100 to ignore loss correctly
+        labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)
+
+        # if bos token is appended in previous tokenization step,
+        # cut bos token here as it's append later anyways
+        if (labels[:, 0] == self.decoder_start_token_id).all().cpu().item():
+            labels = labels[:, 1:]
+
+        batch["labels"] = labels
+
+        return batch
+
+
+def load_maybe_streaming_dataset(dataset_name, dataset_config_name, split="train", streaming=True, **kwargs):
+    """
+    Utility function to load a dataset in streaming mode. For datasets with multiple splits,
+    each split is loaded individually and then splits combined by taking alternating examples from
+    each (interleaving).
+    """
+    if "+" in split:
+        # load multiple splits separated by the `+` symbol with streaming mode
+        dataset_splits = [
+            load_dataset(dataset_name, dataset_config_name, split=split_name, streaming=streaming, **kwargs)
+            for split_name in split.split("+")
+        ]
+        # interleave multiple splits to form one dataset
+        interleaved_dataset = interleave_datasets(dataset_splits)
+        return interleaved_dataset
+    else:
+        # load a single split *with* streaming mode
+        dataset = load_dataset(dataset_name, dataset_config_name, split=split, streaming=streaming, **kwargs)
+        return dataset
+
+
+def main():
+    # 1. Parse input arguments
+    # See all possible arguments in src/transformers/training_args.py
+    # or by passing the --help flag to this script.
+    # We now keep distinct sets of args, for a cleaner separation of concerns.
+    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))
+
+    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+        # If we pass only one argument to the script and it's the path to a json file,
+        # let's parse it to get our arguments.
+        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
+    else:
+        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
+    # information sent is the one passed as arguments along with your Python/PyTorch versions.
+    send_example_telemetry("run_speech_recognition_seq2seq_streaming", model_args, data_args)
+
+    # 2. Setup logging
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        handlers=[logging.StreamHandler(sys.stdout)],
+    )
+    log_level = training_args.get_process_log_level()
+    logger.setLevel(log_level)
+    datasets.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.enable_default_handler()
+    transformers.utils.logging.enable_explicit_format()
+
+    logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)
+
+    # Log on each process the small summary:
+    logger.warning(
+        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+        f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
+    )
+    logger.info(f"Training/evaluation parameters {training_args}")
+
+    # Set the verbosity to info of the Transformers logger (on main process only):
+    if is_main_process(training_args.local_rank):
+        transformers.utils.logging.set_verbosity_info()
+    logger.info("Training/evaluation parameters %s", training_args)
+
+    # 3. Detecting last checkpoint and eventually continue from last checkpoint
+    last_checkpoint = None
+    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
+        last_checkpoint = get_last_checkpoint(training_args.output_dir)
+        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
+            raise ValueError(
+                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
+                "Use --overwrite_output_dir to overcome."
+            )
+        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
+            logger.info(
+                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
+                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
+            )
+
+    # Set seed before initializing model.
+    set_seed(training_args.seed)
+
+    # 4. Load dataset
+    raw_datasets = IterableDatasetDict() if data_args.streaming else DatasetDict()
+
+    if training_args.do_train:
+        raw_datasets["train"] = load_maybe_streaming_dataset(
+            data_args.dataset_name,
+            data_args.dataset_config_name,
+            split=data_args.train_split_name,
+            use_auth_token=True if model_args.use_auth_token else None,
+            streaming=data_args.streaming,
+        )
+
+    if training_args.do_eval:
+        raw_datasets["eval"] = load_maybe_streaming_dataset(
+            data_args.dataset_name,
+            data_args.dataset_config_name,
+            split=data_args.eval_split_name,
+            use_auth_token=True if model_args.use_auth_token else None,
+            streaming=data_args.streaming,
+        )
+
+    raw_datasets_features = list(next(iter(raw_datasets.values())).features.keys())
+
+    if data_args.audio_column_name not in raw_datasets_features:
+        raise ValueError(
+            f"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.dataset_name}'. "
+            "Make sure to set `--audio_column_name` to the correct audio column - one of "
+            f"{', '.join(raw_datasets_features)}."
+        )
+
+    if data_args.text_column_name not in raw_datasets_features:
+        raise ValueError(
+            f"--text_column_name {data_args.text_column_name} not found in dataset '{data_args.dataset_name}'. "
+            "Make sure to set `--text_column_name` to the correct text column - one of "
+            f"{', '.join(raw_datasets_features)}."
+        )
+
+    # 5. Load pretrained model, tokenizer, and feature extractor
+    #
+    # Distributed training:
+    # The .from_pretrained methods guarantee that only one local process can concurrently
+    config = AutoConfig.from_pretrained(
+        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+
+    config.update({"forced_decoder_ids": model_args.forced_decoder_ids, "suppress_tokens": model_args.suppress_tokens})
+
+    if training_args.gradient_checkpointing:
+        config.update({"use_cache": False})
+
+    feature_extractor = AutoFeatureExtractor.from_pretrained(
+        model_args.feature_extractor_name if model_args.feature_extractor_name else model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+    tokenizer = AutoTokenizer.from_pretrained(
+        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        use_fast=model_args.use_fast_tokenizer,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+    model = AutoModelForSpeechSeq2Seq.from_pretrained(
+        model_args.model_name_or_path,
+        config=config,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+
+    if model.config.decoder_start_token_id is None:
+        raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")
+
+    if model_args.freeze_feature_encoder:
+        model.freeze_feature_encoder()
+
+    if model_args.freeze_encoder:
+        model.freeze_encoder()
+
+    if data_args.language is not None:
+        # We only need to set the task id when the language is specified (i.e. in a multilingual setting)
+        tokenizer.set_prefix_tokens(language=data_args.language, task=data_args.task)
+
+    # 6. Resample speech dataset if necessary
+    dataset_sampling_rate = next(iter(raw_datasets.values())).features[data_args.audio_column_name].sampling_rate
+    if dataset_sampling_rate != feature_extractor.sampling_rate:
+        raw_datasets = raw_datasets.cast_column(
+            data_args.audio_column_name, datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate)
+        )
+
+    # 7. Preprocessing the datasets.
+    # We need to read the audio files as arrays and tokenize the targets.
+    max_input_length = data_args.max_duration_in_seconds * feature_extractor.sampling_rate
+    min_input_length = data_args.min_duration_in_seconds * feature_extractor.sampling_rate
+    audio_column_name = data_args.audio_column_name
+    text_column_name = data_args.text_column_name
+    model_input_name = feature_extractor.model_input_names[0]
+    do_lower_case = data_args.do_lower_case
+    do_remove_punctuation = data_args.do_remove_punctuation
+    normalizer = BasicTextNormalizer()  # 'official' text normalizer from OpenAI
+
+    if data_args.max_train_samples is not None:
+        raw_datasets["train"] = (
+            raw_datasets["train"].take(data_args.max_train_samples)
+            if data_args.streaming
+            else raw_datasets["train"].select(range(data_args.max_train_samples))
+        )
+
+    if data_args.max_eval_samples is not None:
+        raw_datasets["eval"] = (
+            raw_datasets["eval"].take(data_args.max_eval_samples)
+            if data_args.streaming
+            else raw_datasets["eval"].select(range(data_args.max_eval_samples))
+        )
+
+    def prepare_dataset(batch):
+        # process audio
+        sample = batch[audio_column_name]
+        inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"])
+        # process audio length
+        batch[model_input_name] = inputs.get(model_input_name)[0]
+        batch["input_length"] = len(sample["array"])
+
+        # process targets
+        input_str = batch[text_column_name].lower() if do_lower_case else batch[text_column_name]
+        if do_remove_punctuation:
+            input_str = normalizer(input_str).strip()
+        batch["labels"] = tokenizer(input_str).input_ids
+        return batch
+
+    with training_args.main_process_first(desc="dataset map pre-processing"):
+        vectorized_datasets = raw_datasets.map(
+            prepare_dataset,
+            remove_columns=raw_datasets_features,
+        ).with_format("torch")
+
+        if training_args.do_train and data_args.streaming:
+            # manually shuffle if streaming (done by the trainer for non-streaming)
+            vectorized_datasets["train"] = vectorized_datasets["train"].shuffle(
+                buffer_size=data_args.shuffle_buffer_size,
+                seed=training_args.seed,
+            )
+
+    # filter training data that is shorter than min_input_length or longer than
+    # max_input_length
+    def is_audio_in_length_range(length):
+        return min_input_length < length < max_input_length
+
+    if training_args.do_train:
+        vectorized_datasets["train"] = vectorized_datasets["train"].filter(
+            is_audio_in_length_range,
+            input_columns=["input_length"],
+        )
+
+    # 8. Load Metric
+    metric = evaluate.load("wer")
+    do_normalize_eval = data_args.do_normalize_eval
+
+    def compute_metrics(pred):
+        pred_ids = pred.predictions
+
+        pred.label_ids[pred.label_ids == -100] = tokenizer.pad_token_id
+
+        pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
+        # we do not want to group tokens when computing the metrics
+        label_str = tokenizer.batch_decode(pred.label_ids, skip_special_tokens=True)
+
+        if do_normalize_eval:
+            pred_str = [normalizer(pred) for pred in pred_str]
+            label_str = [normalizer(label) for label in label_str]
+            # filtering step to only evaluate the samples that correspond to non-zero references:
+            pred_str = [pred_str[i] for i in range(len(pred_str)) if len(label_str[i]) > 0]
+            label_str = [label_str[i] for i in range(len(label_str)) if len(label_str[i]) > 0]
+
+        wer = 100 * metric.compute(predictions=pred_str, references=label_str)
+
+        return {"wer": wer}
+
+    # 9. Create a single speech processor
+    if is_main_process(training_args.local_rank):
+        # save feature extractor, tokenizer and config
+        feature_extractor.save_pretrained(training_args.output_dir)
+        tokenizer.save_pretrained(training_args.output_dir)
+        config.save_pretrained(training_args.output_dir)
+
+    processor = AutoProcessor.from_pretrained(training_args.output_dir)
+
+    # 10. Define data collator
+    data_collator = DataCollatorSpeechSeq2SeqWithPadding(
+        processor=processor,
+        decoder_start_token_id=model.config.decoder_start_token_id,
+    )
+
+    # 11. Configure Trainer
+    # Trainer callback to reinitialise and reshuffle the streamable datasets at the beginning of each epoch
+    # Only required for streaming: Trainer automatically shuffles non-streaming datasets
+    class ShuffleCallback(TrainerCallback):
+        def on_epoch_begin(self, args, state, control, train_dataloader, **kwargs):
+            if isinstance(train_dataloader.dataset, IterableDatasetShard):
+                pass  # set_epoch() is handled by the Trainer
+            elif isinstance(train_dataloader.dataset, IterableDataset):
+                train_dataloader.dataset.set_epoch(train_dataloader.dataset._epoch + 1)
+
+    # Initialize Trainer
+    trainer = Seq2SeqTrainer(
+        model=model,
+        args=training_args,
+        train_dataset=vectorized_datasets["train"] if training_args.do_train else None,
+        eval_dataset=vectorized_datasets["eval"] if training_args.do_eval else None,
+        tokenizer=feature_extractor,
+        data_collator=data_collator,
+        compute_metrics=compute_metrics if training_args.predict_with_generate else None,
+        callbacks=[ShuffleCallback()] if data_args.streaming else None,
+    )
+
+    # 12. Training
+    if training_args.do_train:
+        checkpoint = None
+        if training_args.resume_from_checkpoint is not None:
+            checkpoint = training_args.resume_from_checkpoint
+        elif last_checkpoint is not None:
+            checkpoint = last_checkpoint
+        train_result = trainer.train(resume_from_checkpoint=checkpoint)
+        trainer.save_model()  # Saves the feature extractor too for easy upload
+
+        metrics = train_result.metrics
+        if data_args.max_train_samples:
+            metrics["train_samples"] = data_args.max_train_samples
+        trainer.log_metrics("train", metrics)
+        trainer.save_metrics("train", metrics)
+        trainer.save_state()
+
+    # 13. Evaluation
+    results = {}
+    if training_args.do_eval:
+        logger.info("*** Evaluate ***")
+        metrics = trainer.evaluate(
+            metric_key_prefix="eval",
+            max_length=training_args.generation_max_length,
+            num_beams=training_args.generation_num_beams,
+        )
+        if data_args.max_eval_samples:
+            metrics["eval_samples"] = data_args.max_eval_samples
+
+        trainer.log_metrics("eval", metrics)
+        trainer.save_metrics("eval", metrics)
+
+    # 14. Write Training Stats
+    kwargs = {
+        "finetuned_from": model_args.model_name_or_path,
+        "tasks": "automatic-speech-recognition",
+        "tags": "whisper-event",
+    }
+    if data_args.dataset_name is not None:
+        kwargs["dataset_tags"] = data_args.dataset_name
+        if data_args.dataset_config_name is not None:
+            kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
+        else:
+            kwargs["dataset"] = data_args.dataset_name
+        if "common_voice" in data_args.dataset_name:
+            kwargs["language"] = data_args.dataset_config_name.split('-')[0]
+        if model_args.model_index_name is not None:
+            kwargs["model_name"] = model_args.model_index_name
+
+    if training_args.push_to_hub:
+        trainer.push_to_hub(**kwargs)
+    else:
+        trainer.create_model_card(**kwargs)
+
+    return results
+
+
+if __name__ == "__main__":
+    main()
diff --git a/whisper-fine-tuning-event/special_tokens_map.json b/whisper-fine-tuning-event/special_tokens_map.json
new file mode 100644
index 0000000000000000000000000000000000000000..bf69932dca4b3719b59fdd8f6cc1978109509f6c
--- /dev/null
+++ b/whisper-fine-tuning-event/special_tokens_map.json
@@ -0,0 +1,139 @@
+{
+  "additional_special_tokens": [
+    "<|endoftext|>",
+    "<|startoftranscript|>",
+    "<|en|>",
+    "<|zh|>",
+    "<|de|>",
+    "<|es|>",
+    "<|ru|>",
+    "<|ko|>",
+    "<|fr|>",
+    "<|ja|>",
+    "<|pt|>",
+    "<|tr|>",
+    "<|pl|>",
+    "<|ca|>",
+    "<|nl|>",
+    "<|ar|>",
+    "<|sv|>",
+    "<|it|>",
+    "<|id|>",
+    "<|hi|>",
+    "<|fi|>",
+    "<|vi|>",
+    "<|he|>",
+    "<|uk|>",
+    "<|el|>",
+    "<|ms|>",
+    "<|cs|>",
+    "<|ro|>",
+    "<|da|>",
+    "<|hu|>",
+    "<|ta|>",
+    "<|no|>",
+    "<|th|>",
+    "<|ur|>",
+    "<|hr|>",
+    "<|bg|>",
+    "<|lt|>",
+    "<|la|>",
+    "<|mi|>",
+    "<|ml|>",
+    "<|cy|>",
+    "<|sk|>",
+    "<|te|>",
+    "<|fa|>",
+    "<|lv|>",
+    "<|bn|>",
+    "<|sr|>",
+    "<|az|>",
+    "<|sl|>",
+    "<|kn|>",
+    "<|et|>",
+    "<|mk|>",
+    "<|br|>",
+    "<|eu|>",
+    "<|is|>",
+    "<|hy|>",
+    "<|ne|>",
+    "<|mn|>",
+    "<|bs|>",
+    "<|kk|>",
+    "<|sq|>",
+    "<|sw|>",
+    "<|gl|>",
+    "<|mr|>",
+    "<|pa|>",
+    "<|si|>",
+    "<|km|>",
+    "<|sn|>",
+    "<|yo|>",
+    "<|so|>",
+    "<|af|>",
+    "<|oc|>",
+    "<|ka|>",
+    "<|be|>",
+    "<|tg|>",
+    "<|sd|>",
+    "<|gu|>",
+    "<|am|>",
+    "<|yi|>",
+    "<|lo|>",
+    "<|uz|>",
+    "<|fo|>",
+    "<|ht|>",
+    "<|ps|>",
+    "<|tk|>",
+    "<|nn|>",
+    "<|mt|>",
+    "<|sa|>",
+    "<|lb|>",
+    "<|my|>",
+    "<|bo|>",
+    "<|tl|>",
+    "<|mg|>",
+    "<|as|>",
+    "<|tt|>",
+    "<|haw|>",
+    "<|ln|>",
+    "<|ha|>",
+    "<|ba|>",
+    "<|jw|>",
+    "<|su|>",
+    "<|translate|>",
+    "<|transcribe|>",
+    "<|startoflm|>",
+    "<|startofprev|>",
+    "<|nocaptions|>",
+    "<|notimestamps|>"
+  ],
+  "bos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}
diff --git a/whisper-fine-tuning-event/tokenizer_config.json b/whisper-fine-tuning-event/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..d13b786c04765fb1a06492b53587752cd67665ea
--- /dev/null
+++ b/whisper-fine-tuning-event/tokenizer_config.json
@@ -0,0 +1,12989 @@
+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "50257": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50258": {
+      "content": "<|startoftranscript|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50259": {
+      "content": "<|en|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50260": {
+      "content": "<|zh|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50261": {
+      "content": "<|de|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50262": {
+      "content": "<|es|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50263": {
+      "content": "<|ru|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50264": {
+      "content": "<|ko|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50265": {
+      "content": "<|fr|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50266": {
+      "content": "<|ja|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50267": {
+      "content": "<|pt|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50268": {
+      "content": "<|tr|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50269": {
+      "content": "<|pl|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50270": {
+      "content": "<|ca|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50271": {
+      "content": "<|nl|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50272": {
+      "content": "<|ar|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50273": {
+      "content": "<|sv|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50274": {
+      "content": "<|it|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50275": {
+      "content": "<|id|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50276": {
+      "content": "<|hi|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50277": {
+      "content": "<|fi|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50278": {
+      "content": "<|vi|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50279": {
+      "content": "<|he|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50280": {
+      "content": "<|uk|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50281": {
+      "content": "<|el|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50282": {
+      "content": "<|ms|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50283": {
+      "content": "<|cs|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50284": {
+      "content": "<|ro|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50285": {
+      "content": "<|da|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50286": {
+      "content": "<|hu|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50287": {
+      "content": "<|ta|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50288": {
+      "content": "<|no|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50289": {
+      "content": "<|th|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50290": {
+      "content": "<|ur|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50291": {
+      "content": "<|hr|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50292": {
+      "content": "<|bg|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50293": {
+      "content": "<|lt|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50294": {
+      "content": "<|la|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50295": {
+      "content": "<|mi|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50296": {
+      "content": "<|ml|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50297": {
+      "content": "<|cy|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50298": {
+      "content": "<|sk|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50299": {
+      "content": "<|te|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50300": {
+      "content": "<|fa|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50301": {
+      "content": "<|lv|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50302": {
+      "content": "<|bn|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50303": {
+      "content": "<|sr|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50304": {
+      "content": "<|az|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50305": {
+      "content": "<|sl|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50306": {
+      "content": "<|kn|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50307": {
+      "content": "<|et|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50308": {
+      "content": "<|mk|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50309": {
+      "content": "<|br|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50310": {
+      "content": "<|eu|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50311": {
+      "content": "<|is|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50312": {
+      "content": "<|hy|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50313": {
+      "content": "<|ne|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50314": {
+      "content": "<|mn|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50315": {
+      "content": "<|bs|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50316": {
+      "content": "<|kk|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50317": {
+      "content": "<|sq|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50318": {
+      "content": "<|sw|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50319": {
+      "content": "<|gl|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50320": {
+      "content": "<|mr|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50321": {
+      "content": "<|pa|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50322": {
+      "content": "<|si|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50323": {
+      "content": "<|km|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50324": {
+      "content": "<|sn|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50325": {
+      "content": "<|yo|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50326": {
+      "content": "<|so|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50327": {
+      "content": "<|af|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50328": {
+      "content": "<|oc|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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diff --git a/whisper_info.txt b/whisper_info.txt
new file mode 100644
index 0000000000000000000000000000000000000000..740dbe320aaf3acdfde98eb5418f4d89fa2e44b1
--- /dev/null
+++ b/whisper_info.txt
@@ -0,0 +1,27 @@
+# Preparing a Python venv to enable opening Jupyter notebooks:
+
+cd ~/git_repos/whisper_related/community-events/
+python3 -m venv ~/python_virtual_envs/whisper_fine_tuning
+source ~/python_virtual_envs/whisper_fine_tuning/bin/activate.csh
+alias python_whisper_fine_tuning "source ~/python_virtual_envs/whisper_fine_tuning/bin/activate.csh"
+python_whisper_fine_tuning
+
+pip install --upgrade pip
+pip install jupyter
+pip install -r whisper-fine-tuning-event/requirements.txt
+pip install transformers
+pip install accelerate -U
+
+
+#pip install numpy
+#pip install matplotlib
+#pip install graphviz
+pip install -U "huggingface_hub[cli]"
+git config --global credential.helper store
+
+rehash
+huggingface-cli login
+
+
+jupyter notebook
+==> runs the Jupyter Notebook server (with port 8888) and opens the app in the browser.