{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LBSYoWbi-45k"
   },
   "source": [
    "# **Fine-tuning XLS-R for Multi-Lingual ASR with 🤗 Transformers**\n",
    "\n",
    "***New (11/2021)***: *This blog post has been updated to feature XLSR's successor, called [XLS-R](https://huggingface.co/models?other=xls_r)*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "V7YOT2mnUiea"
   },
   "source": [
    "**Wav2Vec2** is a pretrained model for Automatic Speech Recognition (ASR) and was released in [September 2020](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) by *Alexei Baevski, Michael Auli, and Alex Conneau*.  Soon after the superior performance of Wav2Vec2 was demonstrated on one of the most popular English datasets for ASR, called [LibriSpeech](https://huggingface.co/datasets/librispeech_asr), *Facebook AI* presented a multi-lingual version of Wav2Vec2, called [XLSR](https://arxiv.org/abs/2006.13979). XLSR stands for *cross-lingual speech representations* and refers to model's ability to learn speech representations that are useful across multiple languages.\n",
    "\n",
    "XLSR's successor, simply called **XLS-R** (refering to the [*''XLM-R*](https://ai.facebook.com/blog/-xlm-r-state-of-the-art-cross-lingual-understanding-through-self-supervision/) *for Speech''*), was released in [November 2021](https://ai.facebook.com/blog/xls-r-self-supervised-speech-processing-for-128-languages) by *Arun Babu, Changhan Wang, Andros Tjandra, et al.* XLS-R used almost **half a million** hours of audio data in 128 languages for self-supervised pre-training and comes in sizes ranging from 300 milion up to **two billion** parameters. You can find the pretrained checkpoints on the 🤗 Hub:\n",
    "\n",
    "- [**Wav2Vec2-XLS-R-300M**](https://huggingface.co/facebook/wav2vec2-xls-r-300m)\n",
    "- [**Wav2Vec2-XLS-R-1B**](https://huggingface.co/facebook/wav2vec2-xls-r-1b)\n",
    "- [**Wav2Vec2-XLS-R-2B**](https://huggingface.co/facebook/wav2vec2-xls-r-2b)\n",
    "\n",
    "Similar to [BERT's masked language modeling objective](http://jalammar.github.io/illustrated-bert/), XLS-R learns contextualized speech representations by randomly masking feature vectors before passing them to a transformer network during self-supervised pre-training (*i.e.* diagram on the left below). \n",
    "\n",
    "For fine-tuning, a single linear layer is added on top of the pre-trained network to train the model on labeled data of audio downstream tasks such as speech recognition, speech translation and audio classification (*i.e.* diagram on the right below).\n",
    "\n",
    "![wav2vec2_structure](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/xls_r.png)\n",
    "\n",
    "XLS-R shows impressive improvements over previous state-of-the-art results on both speech recognition, speech translation and speaker/language identification, *cf.* with Table 3-6, Table 7-10, and Table 11-12 respectively of the official [paper](https://ai.facebook.com/blog/xls-r-self-supervised-speech-processing-for-128-languages)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nT_QrfWtsxIz"
   },
   "source": [
    "## Notebook Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kruqixOYHaIo"
   },
   "source": [
    "\n",
    "In this notebook, we will give an in-detail explanation of how XLS-R - more specifically the pre-trained checkpoint [**Wav2Vec2-XLS-R-300M**](https://huggingface.co/facebook/wav2vec2-xls-r-300m) - can be fine-tuned for ASR.  \n",
    "\n",
    "For demonstration purposes, we fine-tune the model on the low resource ASR dataset of [Common Voice](https://huggingface.co/datasets/common_voice) that contains only *ca.* 4h of validated training data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Gx9OdDYrCtQ1"
   },
   "source": [
    "XLS-R is fine-tuned using Connectionist Temporal Classification (CTC), which is an algorithm that is used to train neural networks for sequence-to-sequence problems, such as ASR and handwriting recognition. \n",
    "\n",
    "I highly recommend reading the well-written blog post [*Sequence Modeling with CTC (2017)*](https://distill.pub/2017/ctc/) by Awni Hannun."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wcHuXIaWyHZU"
   },
   "source": [
    "First, let's try to get a good GPU in our colab! With Google Colab's free version it's sadly becoming much harder to get access to a good GPU. With Google Colab Pro, however, one should easily get either a V100 or P100 GPU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "YELVqGxMxnbG",
    "outputId": "862232b0-f688-43fe-e88a-c22ea90c4635"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mon Jan 24 20:49:11 2022       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 470.57.02    Driver Version: 470.57.02    CUDA Version: 11.4     |\n",
      "|-------------------------------+----------------------+----------------------+\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|                               |                      |               MIG M. |\n",
      "|===============================+======================+======================|\n",
      "|   0  Tesla V100S-PCI...  Off  | 00000000:00:06.0 Off |                    0 |\n",
      "| N/A   37C    P0    25W / 250W |      4MiB / 32510MiB |      0%      Default |\n",
      "|                               |                      |                  N/A |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "                                                                               \n",
      "+-----------------------------------------------------------------------------+\n",
      "| Processes:                                                                  |\n",
      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
      "|        ID   ID                                                   Usage      |\n",
      "|=============================================================================|\n",
      "|  No running processes found                                                 |\n",
      "+-----------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "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",
   "metadata": {
    "id": "e335hPmdtASZ"
   },
   "source": [
    "Before we start, let's install `datasets` and `transformers`. Also, we need the `torchaudio` to load audio files and `jiwer` to evaluate our fine-tuned model using the [word error rate (WER)](https://huggingface.co/metrics/wer) metric ${}^1$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "c8eh87Hoee5d"
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "#!pip install datasets==1.13.3\n",
    "#!pip install transformers==4.11.3\n",
    "#!pip install torchaudio==0.10.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html\n",
    "#!pip install librosa\n",
    "#!pip install jiwer\n",
    "#!pip install kaggle\n",
    "#!pip install huggingface_hub==0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0xxt_LwxDQlO"
   },
   "source": [
    "We strongly suggest to upload your training checkpoints directly to the [🤗 Hub](https://huggingface.co/) while training. The [🤗 Hub](https://huggingface.co/) has integrated version control so you can be sure that no model checkpoint is getting lost during training. \n",
    "\n",
    "To do so you have to store your authentication token from the Hugging Face website (sign up [here](https://huggingface.co/join) if you haven't already!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'4.16.0.dev0'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import transformers\n",
    "transformers.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.10.1+cu102'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torchaudio\n",
    "torchaudio.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "Q3Zd2rW_emZF"
   },
   "outputs": [],
   "source": [
    "# Environment settings: \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_column', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "pd.set_option('display.max_seq_items', None)\n",
    "pd.set_option('display.max_colwidth', 500)\n",
    "pd.set_option('expand_frame_repr', True)\n",
    "\n",
    "\n",
    "\n",
    "from datasets import concatenate_datasets, load_dataset, Audio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "mlMSH3T3EazV",
    "outputId": "d80abc22-3477-44aa-b0de-23725be24b31"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "015f86572b764c72a57361e187adacad",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value='<center>\\n<img src=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from huggingface_hub import notebook_login\n",
    "\n",
    "notebook_login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "d6-tBzl-gmYN"
   },
   "outputs": [],
   "source": [
    "!git config --global credential.helper store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "WcR-d83OEkqb"
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "!apt install git-lfs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Mn9swf6EQ9Vd"
   },
   "source": [
    "\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "${}^1$ In the [paper](https://arxiv.org/pdf/2006.13979.pdf), the model was evaluated using the phoneme error rate (PER), but by far the most common metric in ASR is the word error rate (WER). To keep this notebook as general as possible we decided to evaluate the model using WER."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0mW-C1Nt-j7k"
   },
   "source": [
    "## Prepare Data, Tokenizer, Feature Extractor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BeBosnY9BH3e"
   },
   "source": [
    "ASR models transcribe speech to text, which means that we both need a feature extractor that processes the speech signal to the model's input format, *e.g.* a feature vector, and a tokenizer that processes the model's output format to text. \n",
    "\n",
    "In 🤗 Transformers, the XLS-R model is thus accompanied by both a tokenizer, called [Wav2Vec2CTCTokenizer](https://huggingface.co/transformers/master/model_doc/wav2vec2.html#wav2vec2ctctokenizer), and a feature extractor, called [Wav2Vec2FeatureExtractor](https://huggingface.co/transformers/master/model_doc/wav2vec2.html#wav2vec2featureextractor).\n",
    "\n",
    "Let's start by creating the tokenizer to decode the predicted output classes to the output transcription."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sEXEWEJGQPqD"
   },
   "source": [
    "### Create `Wav2Vec2CTCTokenizer`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tWmMikuNEKl_"
   },
   "source": [
    "A pre-trained XLS-R model maps the speech signal to a sequence of context representations as illustrated in the figure above. However, for speech recognition the model has to to map this sequence of context representations to its corresponding transcription which means that a linear layer has to be added on top of the transformer block (shown in yellow in the diagram above). This linear layer is used to classifies each context representation to a token class analogous how, *e.g.*, after pretraining a linear layer is added on top of BERT's embeddings for further classification - *cf.* with *'BERT'* section of this [blog post](https://huggingface.co/blog/warm-starting-encoder-decoder)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "v5oRE8XjIUH3"
   },
   "source": [
    "The output size of this layer corresponds to the number of tokens in the vocabulary, which does **not** depend onXLS-R's pretraining task, but only on the labeled dataset used for fine-tuning. So in the first step, we will take a look at the chosen dataset of Common Voice and define a vocabulary based on the transcriptions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "idBczw8mWzgt"
   },
   "source": [
    "First, let's go to Common Voice [official website](https://commonvoice.mozilla.org/en/datasets) and pick a language to fine-tune XLS-R on. For this notebook, we will use Turkish. \n",
    "\n",
    "For each language-specific dataset, you can find a language code corresponding to your chosen language. On [Common Voice](https://commonvoice.mozilla.org/en/datasets), look for the field \"Version\". The language code then corresponds to the prefix before the underscore. For Turkish, *e.g.* the language code is `\"tr\"`.\n",
    "\n",
    "Great, now we can use 🤗 Datasets' simple API to download the data. The dataset name is `\"common_voice\"`, the configuration name corresponds to the language code, which is `\"tr\"` in our case."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bee4g9rpLxll"
   },
   "source": [
    "Common Voice has many different splits including `invalidated`, which refers to data that was not rated as \"clean enough\" to be considered useful. In this notebook, we will only make use of the splits `\"train\"`, `\"validation\"` and `\"test\"`. \n",
    "\n",
    "Because the Turkish dataset is so small, we will merge both the validation and training data into a training dataset and only use the test data for validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "iE_xMRzCgHlN",
    "outputId": "e1c4d528-0102-4496-fa91-842e90a9a20c"
   },
   "outputs": [],
   "source": [
    "#!git clone https://huggingface.co/datasets/Finnish-NLP/common_voice_fi common_voice_fi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "id": "eEfc3xJWZ7Ma",
    "outputId": "877e25ea-9e39-494c-a6d2-b1962de1b301"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'fbc_audio'...\n",
      "remote: Enumerating objects: 6178, done.\u001b[K\n",
      "remote: Counting objects: 100% (6178/6178), done.\u001b[K\n",
      "remote: Compressing objects: 100% (6175/6175), done.\u001b[K\n",
      "remote: Total 6178 (delta 10), reused 6157 (delta 1)\u001b[KiB/s     \n",
      "Receiving objects: 100% (6178/6178), 3.10 GiB | 36.05 MiB/s, done.\n",
      "Resolving deltas: 100% (10/10), done.\n",
      "Checking out files: 100% (3072/3072), done.\n"
     ]
    }
   ],
   "source": [
    "# then git clone all datasets to local\n",
    "#!rm -r -f common_voice_fi\n",
    "#!git clone https://huggingface.co/datasets/Finnish-NLP/common_voice_fi common_voice_fi\n",
    "#!rm -r -f css10\n",
    "#!git clone https://huggingface.co/datasets/Finnish-NLP/css10 css10\n",
    "#!rm -r -f voxpopuli_fi\n",
    "#!git clone https://huggingface.co/datasets/Finnish-NLP/voxpopuli_fi voxpopuli_fi\n",
    "#!rm -r -f parliament\n",
    "#!git clone https://huggingface.co/datasets/Finnish-NLP/parliament parliament\n",
    "#!rm -r -f aalto_eduskunta_asr\n",
    "#!git clone https://huggingface.co/datasets/Finnish-NLP/aalto_eduskunta_asr aalto_eduskunta_asr\n",
    "# !rm -r -f fbc_audio\n",
    "# !git clone https://huggingface.co/datasets/Finnish-NLP/fbc_audio fbc_audio\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "MhXNtuvyi4G8"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "current_path = os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datasets\n",
    "datasets.set_caching_enabled(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "Pb7NacE59HPT",
    "outputId": "043c749a-7784-4cdc-bc36-4d39394b9099"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using custom data configuration default-0b4c09e3fdd55089\n",
      "Reusing dataset csv (/workspace/.cache/huggingface/datasets/csv/default-0b4c09e3fdd55089/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e2d80edecb534270a0e35321a23f7f86",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/15 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using custom data configuration default-0b4c09e3fdd55089\n",
      "Reusing dataset csv (/workspace/.cache/huggingface/datasets/csv/default-0b4c09e3fdd55089/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f3fa6cfff6aa407dbb95daf76c643afc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/15 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using custom data configuration default-56a298ce84512c81\n",
      "Reusing dataset csv (/workspace/.cache/huggingface/datasets/csv/default-56a298ce84512c81/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e)\n",
      "Using custom data configuration default-f67d0509b5801355\n",
      "Reusing dataset csv (/workspace/.cache/huggingface/datasets/csv/default-f67d0509b5801355/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e)\n",
      "Using custom data configuration default-48162b0899911e73\n",
      "Reusing dataset csv (/workspace/.cache/huggingface/datasets/csv/default-48162b0899911e73/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e)\n",
      "Using custom data configuration default-2d8bb479a250eb6e\n",
      "Reusing dataset csv (/workspace/.cache/huggingface/datasets/csv/default-2d8bb479a250eb6e/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e)\n",
      "Using custom data configuration default-b29ae81bbeedf449\n",
      "Reusing dataset csv (/workspace/.cache/huggingface/datasets/csv/default-b29ae81bbeedf449/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e)\n",
      "Using custom data configuration default-600072e69a93ab50\n",
      "Reusing dataset csv (/workspace/.cache/huggingface/datasets/csv/default-600072e69a93ab50/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e)\n"
     ]
    }
   ],
   "source": [
    "### NEW CSV\n",
    "common_voice_train = load_dataset('csv', data_files=[f'{current_path}/common_voice_fi/common_voice_fi.csv'], delimiter=',', column_names=['client_id','path','sentence','up_votes','down_votes','age','gender','accent','locale','segment','split','audio','dataset_name','filename'], split='train')\n",
    "common_voice_train = common_voice_train.filter(lambda example: example['split'] in ['train', 'dev','other'])\n",
    "common_voice_test = load_dataset('csv', data_files=[f'{current_path}/common_voice_fi/common_voice_fi.csv'], delimiter=',', column_names=['client_id','path','sentence','up_votes','down_votes','age','gender','accent','locale','segment','split','audio','dataset_name','filename'], split='train')\n",
    "common_voice_test = common_voice_test.filter(lambda example: example['split'] in ['test'])\n",
    "\n",
    "common_voice_train = common_voice_train.remove_columns(['client_id', 'up_votes', 'down_votes', 'age', 'gender', 'accent', 'locale', 'segment', 'split'])\n",
    "common_voice_test = common_voice_test.remove_columns(['client_id', 'up_votes', 'down_votes', 'age', 'gender', 'accent', 'locale', 'segment', 'split'])\n",
    "\n",
    "\n",
    "#common_voice_train = load_dataset('csv', data_files=[f'{current_path}/common_voice_fi/common_voice_fi.csv'], delimiter=',', column_names=['path', 'sentence', 'filename','audio', 'dataset_name', ],split='train')\n",
    "#common_voice_test = load_dataset('csv', data_files=[f'{current_path}/common_voice_fi/common_voice_test.csv'], delimiter=',', column_names=['path', 'sentence', 'filename','audio', 'dataset_name'],split='train')\n",
    "\n",
    "css10 = load_dataset('csv', data_files=[f'{current_path}/css10/css10.csv'], delimiter=',', column_names=['path', 'sentence', 'filename','audio', 'dataset_name'],split='train')\n",
    "voxpopuli_fi = load_dataset('csv', data_files=[f'{current_path}/voxpopuli_fi/voxpopuli.csv'], delimiter=',', column_names=['path', 'sentence', 'filename','audio', 'dataset_name'],split='train')\n",
    "parliament = load_dataset('csv', data_files=[f'{current_path}/parliament/parliament.csv'], delimiter=',', column_names=['path', 'sentence', 'filename','audio', 'dataset_name'],split='train')\n",
    "aalto_eduskunta_asr = load_dataset('csv', data_files=[f'{current_path}/aalto_eduskunta_asr/aalto_asr.csv'], delimiter=',', column_names=['path', 'sentence', 'filename','audio', 'dataset_name'],split='train')\n",
    "\n",
    "fbc_monolog = load_dataset('csv', data_files=[f'{current_path}/fbc_audio/fbc_monolog.csv'], delimiter=',', column_names=[\"xmin\",\"xmax\",\"text\",\"filepath\",\"wav_filename\",\"audio_duration\",\"wav_samplepath\",\"predicted_lang\",\"predicted_proba\"],split='train')\n",
    "fbc_dialog = load_dataset('csv', data_files=[f'{current_path}/fbc_audio/fbc_dialog.csv'], delimiter=',', column_names=[\"xmin\",\"xmax\",\"text\",\"filepath\",\"wav_filename\",\"audio_duration\",\"predicted_lang\",\"predicted_proba\",\"wav_samplepath\"],split='train')\n",
    "\n",
    "\n",
    "# ### REMOVE HEADER ROWS\n",
    "## NOT NEEDED FOR NEW\n",
    "#common_voice_train = common_voice_train.select(indices=[i+1 for i in range(len(common_voice_train)+1)])\n",
    "#common_voice_test = common_voice_test.select(indices=[i+1 for i in range(len(common_voice_test)+1)])\n",
    "css10 = css10.select(indices=[i+1 for i in range(len(css10)+1)])\n",
    "voxpopuli_fi = voxpopuli_fi.select(indices=[i+1 for i in range(len(voxpopuli_fi)+1)])\n",
    "parliament = parliament.select(indices=[i+1 for i in range(len(parliament)+1)])\n",
    "fbc_dialog = fbc_dialog.select(indices=[i+1 for i in range(len(fbc_dialog)+1)])\n",
    "fbc_monolog = fbc_monolog.select(indices=[i+1 for i in range(len(fbc_monolog)+1)])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['path', 'sentence', 'audio', 'dataset_name', 'filename', '__index_level_0__'],\n",
       "    num_rows: 7857\n",
       "})"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
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   ]
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   ],
   "source": [
    "#### FIX PATHS\n",
    "def fix_common_voice_path(batch):\n",
    "    batch['path'] = os.getcwd()+ '/common_voice_fi/clips/' + batch['filename']\n",
    "    batch['audio'] = batch['path']\n",
    "    return batch\n",
    "\n",
    "def fix_css10_path(batch):\n",
    "    batch['path'] = os.getcwd()+ '/css10/' + batch['filename']\n",
    "    batch['audio'] = batch['path']\n",
    "    return batch\n",
    "\n",
    "def fix_voxpopuli_path(batch):\n",
    "    batch['path'] = os.getcwd()+ '/voxpopuli_fi/data/' + batch['filename']\n",
    "    batch['audio'] = batch['path']\n",
    "    return batch\n",
    "\n",
    "def fix_parliament_path(batch):\n",
    "    batch['path'] = os.getcwd()+ '/parliament/data/' + batch['filename']\n",
    "    batch['audio'] = batch['path']\n",
    "    return batch\n",
    "\n",
    "def fix_aalto_asr_path(batch):\n",
    "    batch['path'] = os.getcwd()+ '/aalto_eduskunta_asr/Wav_1_10th_of_data/' + batch['filename']\n",
    "    batch['audio'] = batch['path']\n",
    "    return batch\n",
    "\n",
    "def fix_fbc(batch):\n",
    "    batch['path'] = os.getcwd()+ '/fbc_audio' + batch['wav_samplepath'].replace('raw_splitted','')\n",
    "    batch['sentence'] = batch['text']\n",
    "    batch['filename'] = batch['wav_filename']\n",
    "    batch['audio'] = batch['path']\n",
    "    batch['dataset_name'] = 'fbc'\n",
    "    return batch\n",
    "\n",
    "\n",
    "common_voice_train = common_voice_train.map(fix_common_voice_path)\n",
    "common_voice_test = common_voice_test.map(fix_common_voice_path)\n",
    "css10 = css10.map(fix_css10_path)\n",
    "voxpopuli_fi = voxpopuli_fi.map(fix_voxpopuli_path)\n",
    "parliament = parliament.map(fix_parliament_path)\n",
    "aalto_eduskunta_asr = aalto_eduskunta_asr.map(fix_aalto_asr_path)\n",
    "fbc_dialog = fbc_dialog.map(fix_fbc)\n",
    "fbc_monolog = fbc_monolog.map(fix_fbc)\n",
    "\n",
    "fbc_monolog = fbc_monolog.remove_columns([\"xmin\",\"xmax\",\"text\",\"filepath\",\"wav_filename\",\"audio_duration\",\"wav_samplepath\",\"predicted_lang\",\"predicted_proba\"])\n",
    "fbc_dialog = fbc_dialog.remove_columns([\"xmin\",\"xmax\",\"text\",\"filepath\",\"wav_filename\",\"audio_duration\",\"wav_samplepath\",\"predicted_lang\",\"predicted_proba\"])\n",
    "\n",
    "\n",
    "\n"
   ]
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       "model_id": "6bfa5649199f440f94d45e4f9214a17d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "common_voice_train = common_voice_train.filter(lambda example: example['filename'].endswith('.mp3'))\n",
    "common_voice_test = common_voice_test.filter(lambda example: example['filename'].endswith('.mp3'))\n",
    "css10 = css10.filter(lambda example: example['filename'].endswith('.wav'))\n",
    "voxpopuli_fi = voxpopuli_fi.filter(lambda example: example['filename'].endswith('.wav'))\n",
    "parliament = parliament.filter(lambda example: example['filename'].endswith('.wav'))\n",
    "aalto_eduskunta_asr = aalto_eduskunta_asr.filter(lambda example: example['filename'].endswith('.wav'))\n",
    "fbc_monolog = fbc_monolog.filter(lambda example: example['filename'].endswith('.wav'))\n",
    "fbc_dialog = fbc_dialog.filter(lambda example: example['filename'].endswith('.wav'))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "common_voice_train = common_voice_train.filter(lambda x: '…' not in x , input_columns=[\"sentence\"])\n",
    "common_voice_train = common_voice_train.filter(lambda x: 'é' not in x , input_columns=[\"sentence\"])\n",
    "common_voice_train = common_voice_train.filter(lambda x: '–' not in x , input_columns=[\"sentence\"])\n",
    "\n",
    "common_voice_test = common_voice_test.filter(lambda x: '…' not in x , input_columns=[\"sentence\"])\n",
    "common_voice_test = common_voice_test.filter(lambda x: 'é' not in x , input_columns=[\"sentence\"])\n",
    "common_voice_test = common_voice_test.filter(lambda x: '–' not in x , input_columns=[\"sentence\"])\n",
    "\n",
    "css10 = css10.filter(lambda x: '…' not in x , input_columns=[\"sentence\"])\n",
    "css10 = css10.filter(lambda x: 'é' not in x , input_columns=[\"sentence\"])\n",
    "css10 = css10.filter(lambda x: '–' not in x , input_columns=[\"sentence\"])\n",
    "\n",
    "voxpopuli_fi = voxpopuli_fi.filter(lambda x: '…' not in x , input_columns=[\"sentence\"])\n",
    "voxpopuli_fi = voxpopuli_fi.filter(lambda x: 'é' not in x , input_columns=[\"sentence\"])\n",
    "voxpopuli_fi = voxpopuli_fi.filter(lambda x: '–' not in x , input_columns=[\"sentence\"])\n",
    "\n",
    "parliament = parliament.filter(lambda x: '…' not in x , input_columns=[\"sentence\"])\n",
    "parliament = parliament.filter(lambda x: 'é' not in x , input_columns=[\"sentence\"])\n",
    "parliament = parliament.filter(lambda x: '–' not in x , input_columns=[\"sentence\"])\n",
    "\n",
    "aalto_eduskunta_asr = aalto_eduskunta_asr.filter(lambda x: '…' not in x , input_columns=[\"sentence\"])\n",
    "aalto_eduskunta_asr = aalto_eduskunta_asr.filter(lambda x: 'é' not in x , input_columns=[\"sentence\"])\n",
    "aalto_eduskunta_asr = aalto_eduskunta_asr.filter(lambda x: '–' not in x , input_columns=[\"sentence\"])\n",
    "\n",
    "fbc_monolog = fbc_monolog.filter(lambda x: '…' not in x , input_columns=[\"sentence\"])\n",
    "fbc_monolog = fbc_monolog.filter(lambda x: 'é' not in x , input_columns=[\"sentence\"])\n",
    "fbc_monolog = fbc_monolog.filter(lambda x: '–' not in x , input_columns=[\"sentence\"])\n",
    "\n",
    "fbc_dialog = fbc_dialog.filter(lambda x: '…' not in x , input_columns=[\"sentence\"])\n",
    "fbc_dialog = fbc_dialog.filter(lambda x: 'é' not in x , input_columns=[\"sentence\"])\n",
    "fbc_dialog = fbc_dialog.filter(lambda x: '–' not in x , input_columns=[\"sentence\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "mYtm7WHX9IhP",
    "outputId": "86c79f40-8a9a-4d9f-b698-902c216bf283"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'path': '/workspace/xlsr_fi/voxpopuli_fi/data/2016_transformed/20160412-0900-PLENARY-12-fi_20160412-18_37_52_1.wav', 'sentence': 'kuinka monta uutta tietovuotoa me vielä tarvitsemme että veronkierto saadaan loppumaan?', 'filename': '2016_transformed/20160412-0900-PLENARY-12-fi_20160412-18_37_52_1.wav', 'audio': '/workspace/xlsr_fi/voxpopuli_fi/data/2016_transformed/20160412-0900-PLENARY-12-fi_20160412-18_37_52_1.wav', 'dataset_name': 'voxpopuli', '__index_level_0__': 0.0}\n",
      "\n",
      "\n",
      "{'path': '/workspace/xlsr_fi/common_voice_fi/clips/common_voice_fi_25130781.mp3', 'sentence': 'Olen kaksikymmentäkolme vuotta vanha.', 'audio': '/workspace/xlsr_fi/common_voice_fi/clips/common_voice_fi_25130781.mp3', 'dataset_name': 'common_voice_fi', 'filename': 'common_voice_fi_25130781.mp3', '__index_level_0__': 0.0}\n",
      "\n",
      "\n",
      "{'path': '/workspace/xlsr_fi/common_voice_fi/clips/common_voice_fi_25838955.mp3', 'sentence': 'Nyt en missään tapauksessa sinua auta.', 'audio': '/workspace/xlsr_fi/common_voice_fi/clips/common_voice_fi_25838955.mp3', 'dataset_name': 'common_voice_fi', 'filename': 'common_voice_fi_25838955.mp3', '__index_level_0__': 0.0}\n",
      "\n",
      "\n",
      "{'path': '/workspace/xlsr_fi/parliament/data/session_2_SEG_01.wav', 'sentence': 'edustaja timo soini ja kolmekymmentäkuusi muuta edustajaa ovat kahdeskymmenes tammikuuta tehneet valtioneuvostolle välikysymyksen eurokriisin hoidosta ja kreikan tukemisesta annan nyt puheenvuoron välikysymyksen ensimmäiselle allekirjoittajalle edustaja soinille välikysymyksen ', 'filename': 'session_2_SEG_01.wav', 'audio': '/workspace/xlsr_fi/parliament/data/session_2_SEG_01.wav', 'dataset_name': 'parliament', '__index_level_0__': 0.0}\n",
      "\n",
      "\n",
      "{'path': '/workspace/xlsr_fi/css10/ensimmaisetnovellit/ensimmaisetnovellit_0000.wav', 'sentence': 'I. Siihen aikaan, kun isä lampun osti, sanoi hän äidille näinikään: --Äiti hoi, kuulehan,--', 'filename': 'ensimmaisetnovellit/ensimmaisetnovellit_0000.wav', 'audio': '/workspace/xlsr_fi/css10/ensimmaisetnovellit/ensimmaisetnovellit_0000.wav', 'dataset_name': 'css10', '__index_level_0__': 0.0}\n",
      "\n",
      "\n",
      "{'path': '/workspace/xlsr_fi/aalto_eduskunta_asr/Wav_1_10th_of_data/anne_holmlund_02811.wav', 'sentence': 'ensimmäinen olisi fyysinen läsnäolo eli suomalainen poliisimies olisi fyysisesti samassa tilassa kuin vieraan valtion virkamies', 'filename': 'anne_holmlund_02811.wav', 'audio': '/workspace/xlsr_fi/aalto_eduskunta_asr/Wav_1_10th_of_data/anne_holmlund_02811.wav', 'dataset_name': 'aalto_asr', '__index_level_0__': 0.0}\n",
      "\n",
      "\n",
      "{'__index_level_0__': 2.0, 'path': '/workspace/xlsr_fi/fbc_audio/monolog/1DB-224522_0.wav', 'sentence': ' ruotsin ulkoministeri anna lind kuoli puukottajan iskuihin', 'filename': 'monolog/1DB-224522.wav', 'audio': '/workspace/xlsr_fi/fbc_audio/monolog/1DB-224522_0.wav', 'dataset_name': 'fbc'}\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(voxpopuli_fi[0])\n",
    "print('\\n')\n",
    "print(common_voice_train[0])\n",
    "print('\\n')\n",
    "print(common_voice_test[0])\n",
    "print('\\n')\n",
    "print(parliament[0])\n",
    "print('\\n')\n",
    "print(css10[0])\n",
    "print('\\n')\n",
    "print(aalto_eduskunta_asr[0])\n",
    "print('\\n')\n",
    "print(fbc_monolog[0])\n",
    "print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "VDpJWmm00N7q",
    "outputId": "3ff6c780-e4eb-4c88-8840-747362bbf749"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['path', 'sentence', 'audio', 'dataset_name', 'filename', '__index_level_0__'],\n",
       "    num_rows: 7811\n",
       "})"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "common_voice_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "2MMXcWFFgCXU"
   },
   "outputs": [],
   "source": [
    "from datasets import load_dataset, load_metric, Audio\n",
    "\n",
    "#common_voice_train_train = load_dataset(\"common_voice\", \"fi\", split=\"train+validation\")\n",
    "#common_voice_test = load_dataset(\"common_voice\", \"fi\", split=\"test\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ri5y5N_HMANq"
   },
   "source": [
    "Many ASR datasets only provide the target text, `'sentence'` for each audio array `'audio'` and file `'path'`. Common Voice actually provides much more information about each audio file, such as the `'accent'`, etc. Keeping the notebook as general as possible, we only consider the transcribed text for fine-tuning.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "kbyq6lDgQc2a"
   },
   "outputs": [],
   "source": [
    "common_voice_train = common_voice_train.remove_columns([\"__index_level_0__\", \"dataset_name\"])\n",
    "parliament = parliament.remove_columns([\"__index_level_0__\", \"dataset_name\"])\n",
    "voxpopuli_fi = voxpopuli_fi.remove_columns([\"__index_level_0__\", \"dataset_name\"])\n",
    "css10 = css10.remove_columns([\"__index_level_0__\", \"dataset_name\"])\n",
    "aalto_eduskunta_asr = aalto_eduskunta_asr.remove_columns([\"__index_level_0__\", \"dataset_name\"])\n",
    "common_voice_test = common_voice_test.remove_columns([\"__index_level_0__\", \"dataset_name\"])\n",
    "fbc_dialog = fbc_dialog.remove_columns([\"__index_level_0__\", \"dataset_name\"])\n",
    "fbc_monolog = fbc_monolog.remove_columns([\"__index_level_0__\", \"dataset_name\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Go9Hq4e4NDT9"
   },
   "source": [
    "Let's write a short function to display some random samples of the dataset and run it a couple of times to get a feeling for the transcriptions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-06T15:19:20.350565Z",
     "iopub.status.busy": "2022-01-06T15:19:20.349997Z",
     "iopub.status.idle": "2022-01-06T15:19:20.358857Z",
     "shell.execute_reply": "2022-01-06T15:19:20.356846Z",
     "shell.execute_reply.started": "2022-01-06T15:19:20.350523Z"
    },
    "id": "72737oog2F6U"
   },
   "outputs": [],
   "source": [
    "from datasets import ClassLabel\n",
    "import random\n",
    "import pandas as pd\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "def show_random_elements(dataset, num_examples=10):\n",
    "    assert num_examples <= len(dataset), \"Can't pick more elements than there are in the dataset.\"\n",
    "    picks = []\n",
    "    for _ in range(num_examples):\n",
    "        pick = random.randint(0, len(dataset)-1)\n",
    "        while pick in picks:\n",
    "            pick = random.randint(0, len(dataset)-1)\n",
    "        picks.append(pick)\n",
    "    \n",
    "    df = pd.DataFrame(dataset[picks])\n",
    "    display(HTML(df.to_html()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-06T15:19:21.124248Z",
     "iopub.status.busy": "2022-01-06T15:19:21.123983Z",
     "iopub.status.idle": "2022-01-06T15:19:21.139735Z",
     "shell.execute_reply": "2022-01-06T15:19:21.13898Z",
     "shell.execute_reply.started": "2022-01-06T15:19:21.124217Z"
    },
    "id": "K_JUmf3G3b9S",
    "outputId": "f0834700-d5fb-4611-fd30-f76bb5fbbfbd"
   },
   "outputs": [],
   "source": [
    "show_random_elements(common_voice_train.remove_columns([\"path\", \"audio\"]), num_examples=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fowcOllGNNju"
   },
   "source": [
    "Alright! The transcriptions look fairly clean. Having translated the transcribed sentences, it seems that the language corresponds more to written-out text than noisy dialogue. This makes sense considering that [Common Voice](https://huggingface.co/datasets/common_voice) is a crowd-sourced read speech corpus."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vq7OR50LN49m"
   },
   "source": [
    "We can see that the transcriptions contain some special characters, such as `,.?!;:`. Without a language model, it is much harder to classify speech chunks to such special characters because they don't really correspond to a characteristic sound unit. *E.g.*, the letter `\"s\"` has a more or less clear sound, whereas the special character `\".\"` does not.\n",
    "Also in order to understand the meaning of a speech signal, it is usually not necessary to include special characters in the transcription.\n",
    "\n",
    "Let's simply remove all characters that don't contribute to the meaning of a word and cannot really be represented by an acoustic sound and normalize the text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "svKzVJ_hQGK6"
   },
   "outputs": [],
   "source": [
    "import re\n",
    "CHARS_TO_IGNORE = [\",\", \"?\", \"¿\", \".\", \"!\", \"¡\", \";\", \"；\", \":\", '\"\"', \"%\", '\"', \"�\", \"ʿ\", \"·\", \"჻\", \"~\", \"՞\",\n",
    "                   \"؟\", \"،\", \"।\", \"॥\", \"«\", \"»\", \"„\", \"“\", \"”\", \"「\", \"」\", \"‘\", \"’\", \"《\", \"》\", \"(\", \")\", \"[\", \"]\",\n",
    "                   \"{\", \"}\", \"=\", \"`\", \"_\", \"+\", \"<\", \">\", \"…\", \"–\", \"°\", \"´\", \"ʾ\", \"‹\", \"›\", \"©\", \"®\", \"—\", \"→\", \"。\",\n",
    "                   \"、\", \"﹂\", \"﹁\", \"‧\", \"～\", \"﹏\", \"，\", \"｛\", \"｝\", \"（\", \"）\", \"［\", \"］\", \"【\", \"】\", \"‥\", \"〽\",\n",
    "                   \"『\", \"』\", \"〝\", \"〟\", \"⟨\", \"⟩\", \"〜\", \"：\", \"！\", \"？\", \"♪\", \"؛\", \"/\", \"\\\\\", \"º\", \"−\", \"^\", \"ʻ\", \"ˆ\"]\n",
    "\n",
    "\n",
    "chars_to_remove_regex = f\"[{re.escape(''.join(CHARS_TO_IGNORE))}]\"\n",
    "\n",
    "def remove_special_characters(batch):\n",
    "    batch[\"sentence\"] = re.sub(chars_to_remove_regex, '', batch[\"sentence\"]).lower()\n",
    "    return batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "XIHocAuTQbBR",
    "outputId": "b990891f-e1a3-4e7d-ac62-db7cd68261e8"
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   "outputs": [
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    },
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    },
    {
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     "metadata": {},
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    {
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     "metadata": {},
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    },
    {
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       "version_minor": 0
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     },
     "metadata": {},
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    },
    {
     "data": {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "common_voice_train = common_voice_train.map(remove_special_characters)\n",
    "voxpopuli_fi = voxpopuli_fi.map(remove_special_characters)\n",
    "parliament = parliament.map(remove_special_characters)\n",
    "css10 = css10.map(remove_special_characters)\n",
    "aalto_eduskunta_asr = aalto_eduskunta_asr.map(remove_special_characters)\n",
    "common_voice_test = common_voice_test.map(remove_special_characters)\n",
    "fbc_monolog = fbc_monolog.map(remove_special_characters)\n",
    "fbc_dialog = fbc_dialog.map(remove_special_characters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TxnVS9gIhIma"
   },
   "source": [
    "Let's look at the processed text labels again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "id": "RBDRAAYxRE6n",
    "outputId": "37931a1d-975c-4743-ccf5-89f617803410"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'show_random_elements' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-102-83aa64f862c1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mshow_random_elements\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcommon_voice_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove_columns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"path\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"audio\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'show_random_elements' is not defined"
     ]
    }
   ],
   "source": [
    "show_random_elements(common_voice_train.remove_columns([\"path\",\"audio\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jwfaptH5RJwA"
   },
   "source": [
    "Good! This looks better. We have removed most special characters from transcriptions and normalized them to lower-case only.\n",
    "\n",
    "Before finalizing the pre-processing, it is always advantageous to consult a native speaker of the target language to see whether the text can be further simplified. \n",
    "For this blog post, [Merve](https://twitter.com/mervenoyann) was kind enough to take a quick look and noted that \"hatted\" characters - like `â` - aren't really used anymore in Turkish and can be replaced by their \"un-hatted\" equivalent, *e.g.* `a`. \n",
    "\n",
    "This means that we should replace a sentence like `\"yargı sistemi hâlâ sağlıksız\"` to `\"yargı sistemi hala sağlıksız\"`.\n",
    "\n",
    "Let's write another short mapping function to further simplify the text labels. Remember - the simler the text labels, the easier it is for the model to learn to predict those labels.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3ORHDb2Th2TW"
   },
   "source": [
    "In CTC, it is common to classify speech chunks into letters, so we will do the same here. \n",
    "Let's extract all distinct letters of the training and test data and build our vocabulary from this set of letters.\n",
    "\n",
    "We write a mapping function that concatenates all transcriptions into one long transcription and then transforms the string into a set of chars. \n",
    "It is important to pass the argument `batched=True` to the `map(...)` function so that the mapping function has access to all transcriptions at once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "LwCshNbbeRZR"
   },
   "outputs": [],
   "source": [
    "def extract_all_chars(batch):\n",
    "  all_text = \" \".join(batch[\"sentence\"])\n",
    "  vocab = list(set(all_text))\n",
    "  return {\"vocab\": [vocab], \"all_text\": [all_text]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "_m6uUjjcfbjH",
    "outputId": "9c682f7d-4c56-42bb-facc-61ae3caf8f1b"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4551f3bdf57043338405f28cec184686",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "38a3f1f2d00140ef96569812e6deab1b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vocab_train = common_voice_train.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=common_voice_train.column_names)\n",
    "vocab_test = common_voice_test.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=common_voice_test.column_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7oVgE8RZSJNP"
   },
   "source": [
    "Now, we create the union of all distinct letters in the training dataset and test dataset and convert the resulting list into an enumerated dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "aQfneNsmlJI0"
   },
   "outputs": [],
   "source": [
    "vocab_list = list(set(vocab_train[\"vocab\"][0]) | set(vocab_test[\"vocab\"][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7811\n",
      "1595\n"
     ]
    }
   ],
   "source": [
    "print(len(common_voice_train))\n",
    "print(len(common_voice_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(vocab_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['f',\n",
       " 'v',\n",
       " 'å',\n",
       " 'r',\n",
       " 'd',\n",
       " ' ',\n",
       " 'l',\n",
       " 'b',\n",
       " 'k',\n",
       " 'ö',\n",
       " 'z',\n",
       " 'q',\n",
       " \"'\",\n",
       " 'a',\n",
       " 'y',\n",
       " 'e',\n",
       " 'u',\n",
       " 'g',\n",
       " 'c',\n",
       " 'p',\n",
       " 't',\n",
       " 'h',\n",
       " 'm',\n",
       " '-',\n",
       " 'n',\n",
       " 'i',\n",
       " 'x',\n",
       " 'ä',\n",
       " 'w',\n",
       " 'o',\n",
       " 'j',\n",
       " 's']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "_0kRndSvqaKk",
    "outputId": "1ffa4a1e-6b2d-435b-8190-b196624c8726"
   },
   "outputs": [],
   "source": [
    "vocab_dict = {v: k for k, v in enumerate(sorted(vocab_list))}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JOSzbvs9SXT1"
   },
   "source": [
    "Cool, we see that all letters of the alphabet occur in the dataset (which is not really surprising) and we also extracted the special characters `\"\"` and `'`. Note that we did not exclude those special characters because: \n",
    "\n",
    "The model has to learn to predict when a word is finished or else the model prediction would always be a sequence of chars which would make it impossible to separate words from each other.\n",
    "\n",
    "One should always keep in mind that pre-processing is a very important step before training your model. E.g., we don't want our model to differentiate between `a` and `A` just because we forgot to normalize the data. The difference between `a` and `A` does not depend on the \"sound\" of the letter at all, but more on grammatical rules - *e.g.* use a capitalized letter at the beginning of the sentence. So it is sensible to remove the difference between capitalized and non-capitalized letters so that the model has an easier time learning to transcribe speech. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b1fBRCn-TRaO"
   },
   "source": [
    "To make it clearer that `\" \"` has its own token class, we give it a more visible character `|`. In addition, we also add an \"unknown\" token so that the model can later deal with characters not encountered in Common Voice's training set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "npbIbBoLgaFX"
   },
   "outputs": [],
   "source": [
    "vocab_dict[\"|\"] = vocab_dict[\" \"]\n",
    "del vocab_dict[\" \"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_9yCWg4Sd0cb"
   },
   "source": [
    "Finally, we also add a padding token that corresponds to CTC's \"*blank token*\". The \"blank token\" is a core component of the CTC algorithm. For more information, please take a look at the \"Alignment\" section [here](https://distill.pub/2017/ctc/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "znF0bNunsjbl",
    "outputId": "fe0004d2-ac68-41d7-c47b-26f40f7a0246"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab_dict[\"[UNK]\"] = len(vocab_dict)\n",
    "vocab_dict[\"[PAD]\"] = len(vocab_dict)\n",
    "len(vocab_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SFPGfet8U5sL"
   },
   "source": [
    "Cool, now our vocabulary is complete and consists of 39 tokens, which means that the linear layer that we will add on top of the pretrained XLS-R checkpoint will have an output dimension of 39."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1CujRgBNVRaD"
   },
   "source": [
    "Let's now save the vocabulary as a json file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "ehyUoh9vk191"
   },
   "outputs": [],
   "source": [
    "import json\n",
    "with open('vocab.json', 'w') as vocab_file:\n",
    "    json.dump(vocab_dict, vocab_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SHJDaKlIVVim"
   },
   "source": [
    "In a final step, we use the json file to load the vocabulary into an instance of the `Wav2Vec2CTCTokenizer` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "xriFGEWQkO4M",
    "outputId": "6bb95f36-af35-406d-940b-24c836f094e8"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "file ./config.json not found\n",
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    }
   ],
   "source": [
    "from transformers import Wav2Vec2CTCTokenizer\n",
    "\n",
    "tokenizer = Wav2Vec2CTCTokenizer.from_pretrained(\"./\", unk_token=\"[UNK]\", pad_token=\"[PAD]\", word_delimiter_token=\"|\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KvL12DrNV4cx"
   },
   "source": [
    "If one wants to re-use the just created tokenizer with the fine-tuned model of this notebook, it is strongly advised to upload the `tokenizer` to the [🤗 Hub](https://huggingface.co/). Let's call the repo to which we will upload the files\n",
    "`\"wav2vec2-large-xlsr-turkish-demo-colab\"`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "A1XApZBAF2zr"
   },
   "outputs": [],
   "source": [
    "repo_name = \"wav2vec2-xlsr-fi-train-aug-lm-1B\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B1BiezWZF16d"
   },
   "source": [
    "and upload the tokenizer to the [🤗 Hub](https://huggingface.co/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "zytE1175GAKM",
    "outputId": "10aeae85-6a31-4ecc-9f82-224b6383ed73"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/huggingface_hub/hf_api.py:1001: FutureWarning: `create_repo` now takes `token` as an optional positional argument. Be sure to adapt your code!\n",
      "  warnings.warn(\n",
      "Cloning https://huggingface.co/RASMUS/wav2vec2-xlsr-fi-train-aug-lm-1B into local empty directory.\n",
      "To https://huggingface.co/RASMUS/wav2vec2-xlsr-fi-train-aug-lm-1B\n",
      "   2373c21..643faa1  main -> main\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'https://huggingface.co/RASMUS/wav2vec2-xlsr-fi-train-aug-lm-1B/commit/643faa1cd17fd495ee7547952b23a5526648b564'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.push_to_hub(repo_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SwQM8lH_GGuP"
   },
   "source": [
    "Great, you can see the just created repository under `https://huggingface.co/<your-username>/wav2vec2-large-xls-r-300m-tr-colab`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mYcIiR2FQ96i"
   },
   "source": [
    "### Create `Wav2Vec2FeatureExtractor`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y6mDEyW719rx"
   },
   "source": [
    "Speech is a continuous signal and to be treated by computers, it first has to be discretized, which is usually called **sampling**. The sampling rate hereby plays an important role in that it defines how many data points of the speech signal are measured per second. Therefore, sampling with a higher sampling rate results in a better approximation of the *real* speech signal but also necessitates more values per second.\n",
    "\n",
    "A pretrained checkpoint expects its input data to have been sampled more or less from the same distribution as the data it was trained on. The same speech signals sampled at two different rates have a very different distribution, *e.g.*, doubling the sampling rate results in data points being twice as long. Thus, \n",
    "before fine-tuning a pretrained checkpoint of an ASR model, it is crucial to verify that the sampling rate of the data that was used to pretrain the model matches the sampling rate of the dataset used to fine-tune the model.\n",
    "\n",
    "XLS-R was pretrained on audio data of [Babel](http://www.reading.ac.uk/AcaDepts/ll/speechlab/babel/r), \n",
    "[Multilingual LibriSpeech (MLS)](https://huggingface.co/datasets/multilingual_librispeech), [Common Voice](https://huggingface.co/datasets/common_voice), [VoxPopuli](https://arxiv.org/abs/2101.00390), and [VoxLingua107](https://arxiv.org/abs/2011.12998) at a sampling rate of 16kHz. Common Voice, in its original form, has a sampling rate of 48kHz, thus we will have to downsample the fine-tuning data to 16kHz in the following.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KuUbPW7oV-B5"
   },
   "source": [
    "A `Wav2Vec2FeatureExtractor` object requires the following parameters to be instantiated:\n",
    "\n",
    "- `feature_size`: Speech models take a sequence of feature vectors as an input. While the length of this sequence obviously varies, the feature size should not. In the case of Wav2Vec2, the feature size is 1 because the model was trained on the raw speech signal ${}^2$.\n",
    "- `sampling_rate`: The sampling rate at which the model is trained on.\n",
    "- `padding_value`: For batched inference, shorter inputs need to be padded with a specific value\n",
    "- `do_normalize`: Whether the input should be *zero-mean-unit-variance* normalized or not. Usually, speech models perform better when normalizing the input\n",
    "- `return_attention_mask`: Whether the model should make use of an `attention_mask` for batched inference. In general, XLS-R models checkpoints should **always** use the `attention_mask`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "kAR0-2KLkopp"
   },
   "outputs": [],
   "source": [
    "from transformers import Wav2Vec2FeatureExtractor\n",
    "\n",
    "feature_extractor = Wav2Vec2FeatureExtractor(feature_size=1, sampling_rate=16000, padding_value=0.0, do_normalize=True, return_attention_mask=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qUETetgqYC3W"
   },
   "source": [
    "Great, XLS-R's feature extraction pipeline is thereby fully defined!\n",
    "\n",
    "For improved user-friendliness, the feature extractor and tokenizer are *wrapped* into a single `Wav2Vec2Processor` class so that one only needs a `model` and `processor` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "KYZtoW-tlZgl"
   },
   "outputs": [],
   "source": [
    "from transformers import Wav2Vec2Processor\n",
    "\n",
    "processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DrKnYuvDIoOO"
   },
   "source": [
    "Next, we can prepare the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YFmShnl7RE35"
   },
   "source": [
    "### Preprocess Data\n",
    "\n",
    "So far, we have not looked at the actual values of the speech signal but just the transcription. In addition to `sentence`, our datasets include two more column names `path` and `audio`. `path` states the absolute path of the audio file. Let's take a look.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T6ndIjHGFp0W"
   },
   "source": [
    "XLS-R expects the input in the format of a 1-dimensional array of 16 kHz. This means that the audio file has to be loaded and resampled.\n",
    "\n",
    " Thankfully, `datasets` does this automatically by calling the other column `audio`. Let try it out. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WUUTgI1bGHW-"
   },
   "source": [
    "Great, we can see that the audio file has automatically been loaded. This is thanks to the new [`\"Audio\"` feature](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=audio#datasets.Audio) introduced in `datasets == 4.13.3`, which loads and resamples audio files on-the-fly upon calling.\n",
    "\n",
    "In the example above we can see that the audio data is loaded with a sampling rate of 48kHz whereas 16kHz are expected by the model. We can set the audio feature to the correct sampling rate by making use of [`cast_column`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=cast_column#datasets.DatasetDict.cast_column):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = common_voice_train.select([277])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'path': '/workspace/xlsr_fi/common_voice_fi/clips/common_voice_fi_24124649.mp3',\n",
       " 'sentence': 'eikö sitten ole vanha',\n",
       " 'audio': '/workspace/xlsr_fi/common_voice_fi/clips/common_voice_fi_24124649.mp3',\n",
       " 'filename': 'common_voice_fi_24124649.mp3'}"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchaudio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16000\n",
      "22050\n",
      "16000\n",
      "16000\n",
      "32000\n",
      "32000\n"
     ]
    }
   ],
   "source": [
    "speech_array, sampling_rate = torchaudio.load(voxpopuli_fi[3][\"path\"])\n",
    "print(sampling_rate)\n",
    "speech_array, sampling_rate = torchaudio.load(css10[4][\"path\"])\n",
    "print(sampling_rate)\n",
    "speech_array, sampling_rate = torchaudio.load(aalto_eduskunta_asr[5][\"path\"])\n",
    "print(sampling_rate)\n",
    "speech_array, sampling_rate = torchaudio.load(parliament[4][\"path\"])\n",
    "print(sampling_rate)\n",
    "speech_array, sampling_rate = torchaudio.load(common_voice_train[277][\"path\"])\n",
    "print(sampling_rate)\n",
    "speech_array, sampling_rate = torchaudio.load(common_voice_test[0][\"path\"])\n",
    "print(sampling_rate)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "common_voice_train = concatenate_datasets([common_voice_train, voxpopuli_fi])\n",
    "common_voice_train = concatenate_datasets([common_voice_train, parliament])\n",
    "common_voice_train = concatenate_datasets([common_voice_train, css10])\n",
    "common_voice_train = concatenate_datasets([common_voice_train, aalto_eduskunta_asr])\n",
    "common_voice_train = concatenate_datasets([common_voice_train, fbc_monolog])\n",
    "common_voice_train = concatenate_datasets([common_voice_train, fbc_dialog])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "rrv65aj7G95i"
   },
   "outputs": [],
   "source": [
    "common_voice_train_audio = common_voice_train.cast_column(\"audio\", Audio(sampling_rate=16_000))\n",
    "common_voice_test_audio = common_voice_test.cast_column(\"audio\", Audio(sampling_rate=16_000))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'path': '/workspace/xlsr_fi/common_voice_fi/clips/common_voice_fi_25130781.mp3',\n",
       " 'array': array([ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,\n",
       "         3.7312715e-05,  1.2023805e-05, -3.0640190e-06], dtype=float32),\n",
       " 'sampling_rate': 16000}"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "common_voice_train_audio[0][\"audio\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PcnO4x-NGBEi"
   },
   "source": [
    "Let's take a look at `\"audio\"` again."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SOckzFd4Mbzq"
   },
   "source": [
    "This seemed to have worked! Let's listen to a couple of audio files to better understand the dataset and verify that the audio was correctly loaded. \n",
    "\n",
    "**Note**: *You can click the following cell a couple of times to listen to different speech samples.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12530\n",
      "{'path': '/workspace/xlsr_fi/css10/gulliverin_matkat_kaukaisilla_mailla/gulliverin_matkat_kaukaisilla_mailla_1015.wav', 'sentence': 'hänen majesteetinsa keisarillisen perheensä kanssa ja seurassaan hovin korkeat virkamiehet', 'audio': {'path': '/workspace/xlsr_fi/css10/gulliverin_matkat_kaukaisilla_mailla/gulliverin_matkat_kaukaisilla_mailla_1015.wav', 'array': array([-0.00083112, -0.00110539, -0.00092473, ...,  0.00061306,\n",
      "        0.00055866,  0.        ], dtype=float32), 'sampling_rate': 16000}, 'filename': 'gulliverin_matkat_kaukaisilla_mailla/gulliverin_matkat_kaukaisilla_mailla_1015.wav'}\n",
      "hänen majesteetinsa keisarillisen perheensä kanssa ja seurassaan hovin korkeat virkamiehet\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio  controls=\"controls\" autoplay=\"autoplay\">\n",
       "                    <source 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\" type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import IPython.display as ipd\n",
    "import numpy as np\n",
    "import random\n",
    "\n",
    "rand_int = random.randint(0, len(common_voice_train_audio)-1)\n",
    "print(rand_int)\n",
    "\n",
    "print(common_voice_train_audio[rand_int])\n",
    "print(common_voice_train_audio[rand_int][\"sentence\"])\n",
    "ipd.Audio(data=common_voice_train_audio[rand_int][\"audio\"][\"array\"], autoplay=True, rate=16000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gY8m3vARHYTa"
   },
   "source": [
    "It seems like the data is now correctly loaded and resampled. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1MaL9J2dNVtG"
   },
   "source": [
    "It can be heard, that the speakers change along with their speaking rate, accent, and background environment, etc. Overall, the recordings sound acceptably clear though, which is to be expected from a crowd-sourced read speech corpus.\n",
    "\n",
    "Let's do a final check that the data is correctly prepared, by printing the shape of the speech input, its transcription, and the corresponding sampling rate.\n",
    "\n",
    "**Note**: *You can click the following cell a couple of times to verify multiple samples.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Architecture:        x86_64\n",
      "CPU op-mode(s):      32-bit, 64-bit\n",
      "Byte Order:          Little Endian\n",
      "CPU(s):              60\n",
      "On-line CPU(s) list: 0-59\n",
      "Thread(s) per core:  1\n",
      "Core(s) per socket:  1\n",
      "Socket(s):           60\n",
      "NUMA node(s):        1\n",
      "Vendor ID:           GenuineIntel\n",
      "CPU family:          6\n",
      "Model:               85\n",
      "Model name:          Intel(R) Xeon(R) Gold 6226R CPU @ 2.90GHz\n",
      "Stepping:            7\n",
      "CPU MHz:             2893.208\n",
      "BogoMIPS:            5786.41\n",
      "Virtualization:      VT-x\n",
      "Hypervisor vendor:   KVM\n",
      "Virtualization type: full\n",
      "L1d cache:           32K\n",
      "L1i cache:           32K\n",
      "L2 cache:            4096K\n",
      "L3 cache:            16384K\n",
      "NUMA node0 CPU(s):   0-59\n",
      "Flags:               fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology cpuid tsc_known_freq pni pclmulqdq vmx ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat umip pku ospke avx512_vnni md_clear arch_capabilities\n"
     ]
    }
   ],
   "source": [
    "!lscpu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_dataset(batch):\n",
    "    audio = batch[\"audio\"]\n",
    "\n",
    "    # batched output is \"un-batched\"\n",
    "    batch[\"input_values\"] = processor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_values[0]\n",
    "    batch[\"input_length\"] = len(batch[\"input_values\"])\n",
    "    \n",
    "    with processor.as_target_processor():\n",
    "        batch[\"labels\"] = processor(batch[\"sentence\"]).input_ids\n",
    "    return batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done train\n"
     ]
    }
   ],
   "source": [
    "common_voice_train_audio = common_voice_train_audio.map(prepare_dataset, remove_columns=common_voice_train_audio.column_names, num_proc=48)\n",
    "print(\"done train\")\n",
    "common_voice_test_audio = common_voice_test_audio.map(prepare_dataset, remove_columns=common_voice_test_audio.column_names, num_proc=48)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['input_values', 'input_length', 'labels'],\n",
       "    num_rows: 19453\n",
       "})"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "common_voice_train_audio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9439\n",
      "[15, 3, 3, 22, 3, 14, 17, 23, 21, 24, 3, 14, 11, 17, 13, 23, 16, 16, 3, 16, 0, 24, 3, 14, 15, 11, 21, 22, 7, 14, 11, 12, 3, 16, 3, 0, 22, 3, 10, 6, 17, 16, 0, 3, 14, 14, 7, 24, 11, 11, 24, 3, 22, 3, 0, 20, 23, 17, 13, 3, 0, 12, 3, 0, 14, 23, 17, 16, 16, 17, 16, 24, 3, 20, 3, 13, 14, 23, 21, 22, 7, 20, 11, 16, 0, 15, 7, 20, 13, 11, 22, 27, 21, 22, 29]\n"
     ]
    },
    {
     "data": {
      "text/html": [
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\" type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import IPython.display as ipd\n",
    "import numpy as np\n",
    "import random\n",
    "\n",
    "rand_int = random.randint(0, len(common_voice_train_audio)-1)\n",
    "print(rand_int)\n",
    "\n",
    "print(common_voice_train_audio[rand_int][\"labels\"])\n",
    "ipd.Audio(data=common_voice_train_audio[rand_int][\"input_values\"], autoplay=True, rate=16000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1309\n",
      "[29, 29, 16, 7, 21, 22, 29, 15, 15, 7, 0, 21, 11, 13, 21, 11, 0, 22, 29, 22, 29, 0, 15, 11, 7, 22, 11, 16, 22, 31, 29, 0, 24, 3, 21, 22, 3, 3, 16]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio  controls=\"controls\" autoplay=\"autoplay\">\n",
       "                    <source 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\" type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import IPython.display as ipd\n",
    "import numpy as np\n",
    "import random\n",
    "\n",
    "rand_int = random.randint(0, len(common_voice_test_audio)-1)\n",
    "print(rand_int)\n",
    "\n",
    "print(common_voice_test_audio[rand_int][\"labels\"])\n",
    "ipd.Audio(data=common_voice_test_audio[rand_int][\"input_values\"], autoplay=True, rate=16000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "M9teZcSwOBJ4"
   },
   "source": [
    "Good! Everything looks fine - the data is a 1-dimensional array, the sampling rate always corresponds to 16kHz, and the target text is normalized."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "k3Pbn5WvOYZF"
   },
   "source": [
    "Finally, we can leverage `Wav2Vec2Processor` to process the data to the format expected by `Wav2Vec2ForCTC` for training. To do so let's make use of Dataset's [`map(...)`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=map#datasets.DatasetDict.map) function.\n",
    "\n",
    "First, we load and resample the audio data, simply by calling `batch[\"audio\"]`.\n",
    "Second, we extract the `input_values` from the loaded audio file. In our case, the `Wav2Vec2Processor` only normalizes the data. For other speech models, however, this step can include more complex feature extraction, such as [Log-Mel feature extraction](https://en.wikipedia.org/wiki/Mel-frequency_cepstrum). \n",
    "Third, we encode the transcriptions to label ids.\n",
    "\n",
    "**Note**: This mapping function is a good example of how the `Wav2Vec2Processor` class should be used. In \"normal\" context, calling `processor(...)` is redirected to `Wav2Vec2FeatureExtractor`'s call method. When wrapping the processor into the `as_target_processor` context, however, the same method is redirected to `Wav2Vec2CTCTokenizer`'s call method.\n",
    "For more information please check the [docs](https://huggingface.co/transformers/master/model_doc/wav2vec2.html#transformers.Wav2Vec2Processor.__call__)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "q6Pg_WR3OGAP"
   },
   "source": [
    "Let's apply the data preparation function to all examples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nKcEWHvKI1by"
   },
   "source": [
    "**Note**: Currently `datasets` make use of [`torchaudio`](https://pytorch.org/audio/stable/index.html) and [`librosa`](https://librosa.org/doc/latest/index.html) for audio loading and resampling. If you wish to implement your own costumized data loading/sampling, feel free to just make use of the `\"path\"` column instead and disregard the `\"audio\"` column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "24CxHd5ewI4T"
   },
   "source": [
    "Long input sequences require a lot of memory. XLS-R is based on `self-attention` the memory requirement scales quadratically with the input length for long input sequences (*cf.* with [this](https://www.reddit.com/r/MachineLearning/comments/genjvb/d_why_is_the_maximum_input_sequence_length_of/) reddit post). In case this demo crashes with an \"Out-of-memory\" error for you, you might want to uncomment the following lines to filter all sequences that are longer than 5 seconds for training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['path', 'sentence', 'audio', 'filename'],\n",
       "    num_rows: 19453\n",
       "})"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "common_voice_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "augmented_samples_to_add = common_voice_train_audio.select([random.randint(0,19453) for i in range(3000)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['input_values', 'input_length', 'labels'],\n",
       "    num_rows: 3000\n",
       "})"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "augmented_samples_to_add"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from audiomentations import Compose, AddGaussianNoise, TimeStretch, PitchShift, Shift\n",
    "\n",
    "# Augment method suggested by HF community member, TODO: better augmentation\n",
    "augment = Compose([\n",
    "  AddGaussianNoise(min_amplitude=0.001, max_amplitude=0.008, p=0.5),\n",
    "  TimeStretch(min_rate=0.85, max_rate=1.15, p=0.5),\n",
    "  Shift(min_fraction=-0.5, max_fraction=0.5, p=0.5)\n",
    "    \n",
    "\n",
    "])\n",
    "\n",
    "def augment_dataset(batch):\n",
    "    batch[\"input_values\"] = augment(samples=np.asarray(batch[\"input_values\"]), sample_rate=16000)\n",
    "    return batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/audiomentations/core/transforms_interface.py:57: UserWarning: Warning: input samples have np.float64 dtype. Converting to np.float32...\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.8/site-packages/audiomentations/core/transforms_interface.py:57: UserWarning: Warning: input samples have np.float64 dtype. Converting to np.float32...\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.8/site-packages/audiomentations/core/transforms_interface.py:57: UserWarning: Warning: input samples have np.float64 dtype. Converting to np.float32...\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.8/site-packages/audiomentations/core/transforms_interface.py:57: UserWarning: Warning: input samples have np.float64 dtype. Converting to np.float32...\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.8/site-packages/audiomentations/core/transforms_interface.py:57: UserWarning: Warning: input samples have np.float64 dtype. Converting to np.float32...\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.8/site-packages/audiomentations/core/transforms_interface.py:57: UserWarning: Warning: input samples have np.float64 dtype. Converting to np.float32...\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.8/site-packages/audiomentations/core/transforms_interface.py:57: UserWarning: Warning: input samples have np.float64 dtype. Converting to np.float32...\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.8/site-packages/audiomentations/core/transforms_interface.py:57: UserWarning: Warning: input samples have np.float64 dtype. Converting to np.float32...\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "augmented_samples_to_add = augmented_samples_to_add.map(augment_dataset, num_proc=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2966\n",
      "[0, 24, 7, 16, 29, 12, 29, 16, 0, 22, 23, 13, 7, 15, 3, 22, 0, 3, 4, 10, 3, 3, 21, 11, 3, 0, 12, 3, 0, 7, 22, 7, 14, 29, 2, 17, 21, 21, 7, 22, 11, 3, 0, 17, 24, 3, 22, 0, 12, 17, 0, 18, 11, 22, 13, 29, 29, 16, 0, 18, 27, 20, 13, 11, 16, 7, 7, 22, 0, 7, 20, 17, 17, 16, 0, 9, 7, 17, 20, 9, 11, 3, 21, 22, 3]\n"
     ]
    },
    {
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\" type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import IPython.display as ipd\n",
    "import numpy as np\n",
    "import random\n",
    "\n",
    "rand_int = random.randint(0, len(augmented_samples_to_add)-1)\n",
    "print(rand_int)\n",
    "\n",
    "print(augmented_samples_to_add[rand_int][\"labels\"])\n",
    "ipd.Audio(data=augmented_samples_to_add[rand_int][\"input_values\"], autoplay=True, rate=16000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['input_values', 'input_length', 'labels'],\n",
       "    num_rows: 3000\n",
       "})"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "augmented_samples_to_add"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "common_voice_train_audio_augmented = concatenate_datasets([common_voice_train_audio, augmented_samples_to_add])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['input_values', 'input_length', 'labels'],\n",
       "    num_rows: 22453\n",
       "})"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "common_voice_train_audio_augmented"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "tdHfbUJ_09iA"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train before filtering:\n",
      "Dataset({\n",
      "    features: ['input_values', 'input_length', 'labels'],\n",
      "    num_rows: 22453\n",
      "})\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c088e77593c345f496faf74b7a374823",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/23 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1be91e8c854944c79a58a4380a8875db",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/23 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train after filtering:\n",
      "Dataset({\n",
      "    features: ['input_values', 'input_length', 'labels'],\n",
      "    num_rows: 22174\n",
      "})\n"
     ]
    }
   ],
   "source": [
    "max_input_length_in_sec = 30\n",
    "min_input_length_in_sec = 2\n",
    "print('train before filtering:')\n",
    "print(common_voice_train_audio_augmented)\n",
    "common_voice_train_audio_augmented = common_voice_train_audio_augmented.filter(lambda x: x < max_input_length_in_sec * processor.feature_extractor.sampling_rate, input_columns=[\"input_length\"])\n",
    "common_voice_train_audio_augmented = common_voice_train_audio_augmented.filter(lambda x: x > min_input_length_in_sec * processor.feature_extractor.sampling_rate, input_columns=[\"input_length\"])\n",
    "print('train after filtering:')\n",
    "print(common_voice_train_audio_augmented)\n",
    "# print('\\n')\n",
    "# print('test before filtering:')\n",
    "# print(common_voice_test_audio)\n",
    "# common_voice_test_audio = common_voice_test_audio.filter(lambda x: x < max_input_length_in_sec * processor.feature_extractor.sampling_rate, input_columns=[\"input_length\"])\n",
    "# common_voice_test_audio = common_voice_test_audio.filter(lambda x: x > min_input_length_in_sec * processor.feature_extractor.sampling_rate, input_columns=[\"input_length\"])\n",
    "# print('test after filtering:')\n",
    "# print(common_voice_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1ZWDCCKqwcfS"
   },
   "source": [
    "Awesome, now we are ready to start training!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gYlQkKVoRUos"
   },
   "source": [
    "## Training\n",
    "\n",
    "The data is processed so that we are ready to start setting up the training pipeline. We will make use of 🤗's [Trainer](https://huggingface.co/transformers/master/main_classes/trainer.html?highlight=trainer) for which we essentially need to do the following:\n",
    "\n",
    "- Define a data collator. In contrast to most NLP models, XLS-R has a much larger input length than output length. *E.g.*, a sample of input length 50000 has an output length of no more than 100. Given the large input sizes, it is much more efficient to pad the training batches dynamically meaning that all training samples should only be padded to the longest sample in their batch and not the overall longest sample. Therefore, fine-tuning XLS-R requires a special padding data collator, which we will define below\n",
    "\n",
    "- Evaluation metric. During training, the model should be evaluated on the word error rate. We should define a `compute_metrics` function accordingly\n",
    "\n",
    "- Load a pretrained checkpoint. We need to load a pretrained checkpoint and configure it correctly for training.\n",
    "\n",
    "- Define the training configuration.\n",
    "\n",
    "After having fine-tuned the model, we will correctly evaluate it on the test data and verify that it has indeed learned to correctly transcribe speech."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Slk403unUS91"
   },
   "source": [
    "### Set-up Trainer\n",
    "\n",
    "Let's start by defining the data collator. The code for the data collator was copied from [this example](https://github.com/huggingface/transformers/blob/7e61d56a45c19284cfda0cee8995fb552f6b1f4e/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py#L219).\n",
    "\n",
    "Without going into too many details, in contrast to the common data collators, this data collator treats the `input_values` and `labels` differently and thus applies to separate padding functions on them (again making use of XLS-R processor's context manager). This is necessary because in speech input and output are of different modalities meaning that they should not be treated by the same padding function.\n",
    "Analogous to the common data collators, the padding tokens in the labels with `-100` so that those tokens are **not** taken into account when computing the loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "tborvC9hx88e"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "from dataclasses import dataclass, field\n",
    "from typing import Any, Dict, List, Optional, Union\n",
    "\n",
    "@dataclass\n",
    "class DataCollatorCTCWithPadding:\n",
    "    \"\"\"\n",
    "    Data collator that will dynamically pad the inputs received.\n",
    "    Args:\n",
    "        processor (:class:`~transformers.Wav2Vec2Processor`)\n",
    "            The processor used for proccessing the data.\n",
    "        padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):\n",
    "            Select a strategy to pad the returned sequences (according to the model's padding side and padding index)\n",
    "            among:\n",
    "            * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single\n",
    "              sequence if provided).\n",
    "            * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the\n",
    "              maximum acceptable input length for the model if that argument is not provided.\n",
    "            * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of\n",
    "              different lengths).\n",
    "    \"\"\"\n",
    "\n",
    "    processor: Wav2Vec2Processor\n",
    "    padding: Union[bool, str] = True\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 lenghts and need\n",
    "        # different padding methods\n",
    "        input_features = [{\"input_values\": feature[\"input_values\"]} for feature in features]\n",
    "        label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n",
    "\n",
    "        batch = self.processor.pad(\n",
    "            input_features,\n",
    "            padding=self.padding,\n",
    "            return_tensors=\"pt\",\n",
    "        )\n",
    "        with self.processor.as_target_processor():\n",
    "            labels_batch = self.processor.pad(\n",
    "                label_features,\n",
    "                padding=self.padding,\n",
    "                return_tensors=\"pt\",\n",
    "            )\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",
    "        batch[\"labels\"] = labels\n",
    "\n",
    "        return batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "lbQf5GuZyQ4_"
   },
   "outputs": [],
   "source": [
    "data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xO-Zdj-5cxXp"
   },
   "source": [
    "Next, the evaluation metric is defined. As mentioned earlier, the \n",
    "predominant metric in ASR is the word error rate (WER), hence we will use it in this notebook as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "9Xsux2gmyXso",
    "outputId": "4ae12795-d6ac-4b51-ff84-748c8a3c8bc9"
   },
   "outputs": [],
   "source": [
    "wer_metric = load_metric(\"wer\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E1qZU5p-deqB"
   },
   "source": [
    "The model will return a sequence of logit vectors:\n",
    "$\\mathbf{y}_1, \\ldots, \\mathbf{y}_m$ with $\\mathbf{y}_1 = f_{\\theta}(x_1, \\ldots, x_n)[0]$ and $n >> m$.\n",
    "\n",
    "A logit vector $\\mathbf{y}_1$ contains the log-odds for each word in the vocabulary we defined earlier, thus $\\text{len}(\\mathbf{y}_i) =$ `config.vocab_size`. We are interested in the most likely prediction of the model and thus take the `argmax(...)` of the logits. Also, we transform the encoded labels back to the original string by replacing `-100` with the `pad_token_id` and decoding the ids while making sure that consecutive tokens are **not** grouped to the same token in CTC style ${}^1$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "1XZ-kjweyTy_"
   },
   "outputs": [],
   "source": [
    "def compute_metrics(pred):\n",
    "    pred_logits = pred.predictions\n",
    "    pred_ids = np.argmax(pred_logits, axis=-1)\n",
    "\n",
    "    pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id\n",
    "\n",
    "    pred_str = processor.batch_decode(pred_ids)\n",
    "    # we do not want to group tokens when computing the metrics\n",
    "    label_str = processor.batch_decode(pred.label_ids, group_tokens=False)\n",
    "\n",
    "    wer = wer_metric.compute(predictions=pred_str, references=label_str)\n",
    "\n",
    "    return {\"wer\": wer}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Xmgrx4bRwLIH"
   },
   "source": [
    "Now, we can load the pretrained checkpoint of [Wav2Vec2-XLS-R-300M](https://huggingface.co/facebook/wav2vec2-xls-r-300m). The tokenizer's `pad_token_id` must be to define the model's `pad_token_id` or in the case of `Wav2Vec2ForCTC` also CTC's *blank token* ${}^2$. To save GPU memory, we enable PyTorch's [gradient checkpointing](https://pytorch.org/docs/stable/checkpoint.html) and also set the loss reduction to \"*mean*\".\n",
    "\n",
    "Because the dataset is quite small (~6h of training data) and because Common Voice is quite noisy, fine-tuning Facebook's [wav2vec2-xls-r-300m checkpoint](https://huggingface.co/facebook/wav2vec2-xls-r-300m) seems to require some hyper-parameter tuning. Therefore, I had to play around a bit with different values for dropout, [SpecAugment](https://arxiv.org/abs/1904.08779)'s masking dropout rate, layer dropout, and the learning rate until training seemed to be stable enough. \n",
    "\n",
    "**Note**: When using this notebook to train XLS-R on another language of Common Voice those hyper-parameter settings might not work very well. Feel free to adapt those depending on your use case. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Wav2Vec2ForCTC(\n",
       "  (wav2vec2): Wav2Vec2Model(\n",
       "    (feature_extractor): Wav2Vec2FeatureEncoder(\n",
       "      (conv_layers): ModuleList(\n",
       "        (0): Wav2Vec2LayerNormConvLayer(\n",
       "          (conv): Conv1d(1, 512, kernel_size=(10,), stride=(5,))\n",
       "          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (1): Wav2Vec2LayerNormConvLayer(\n",
       "          (conv): Conv1d(512, 512, kernel_size=(3,), stride=(2,))\n",
       "          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (2): Wav2Vec2LayerNormConvLayer(\n",
       "          (conv): Conv1d(512, 512, kernel_size=(3,), stride=(2,))\n",
       "          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (3): Wav2Vec2LayerNormConvLayer(\n",
       "          (conv): Conv1d(512, 512, kernel_size=(3,), stride=(2,))\n",
       "          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (4): Wav2Vec2LayerNormConvLayer(\n",
       "          (conv): Conv1d(512, 512, kernel_size=(3,), stride=(2,))\n",
       "          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (5): Wav2Vec2LayerNormConvLayer(\n",
       "          (conv): Conv1d(512, 512, kernel_size=(2,), stride=(2,))\n",
       "          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (6): Wav2Vec2LayerNormConvLayer(\n",
       "          (conv): Conv1d(512, 512, kernel_size=(2,), stride=(2,))\n",
       "          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (feature_projection): Wav2Vec2FeatureProjection(\n",
       "      (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "      (projection): Linear(in_features=512, out_features=1280, bias=True)\n",
       "      (dropout): Dropout(p=0.04, inplace=False)\n",
       "    )\n",
       "    (encoder): Wav2Vec2EncoderStableLayerNorm(\n",
       "      (pos_conv_embed): Wav2Vec2PositionalConvEmbedding(\n",
       "        (conv): Conv1d(1280, 1280, kernel_size=(128,), stride=(1,), padding=(64,), groups=16)\n",
       "        (padding): Wav2Vec2SamePadLayer()\n",
       "      )\n",
       "      (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "      (dropout): Dropout(p=0.047, inplace=False)\n",
       "      (layers): ModuleList(\n",
       "        (0): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (1): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (2): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (3): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (4): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (5): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (6): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (7): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (8): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (9): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (10): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (11): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (12): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (13): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (14): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (15): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (16): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (17): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (18): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (19): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (20): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (21): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (22): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (23): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (24): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (25): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (26): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (27): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (28): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (29): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (30): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (31): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (32): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (33): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (34): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (35): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (36): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (37): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (38): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (39): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (40): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (41): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (42): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (43): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (44): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (45): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (46): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (47): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (dropout): Dropout(p=0.0, inplace=False)\n",
       "  (lm_head): Linear(in_features=1280, out_features=36, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from transformers import Wav2Vec2ForCTC\n",
    "\n",
    "model = Wav2Vec2ForCTC.from_pretrained(\n",
    "    \"wav2vec2-xlsr-fi-train-aug-lm-1B\", \n",
    "    attention_dropout=0.094,\n",
    "    hidden_dropout=0.047,\n",
    "    feat_proj_dropout=0.04,\n",
    "    mask_time_prob=0.082,\n",
    "    layerdrop=0.041,\n",
    "    activation_dropout=0.055,\n",
    "    ctc_loss_reduction=\"mean\", \n",
    "    pad_token_id=processor.tokenizer.pad_token_id,\n",
    "    vocab_size=len(processor.tokenizer),\n",
    ")\n",
    "model.to('cuda')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": true,
    "id": "e7cqAWIayn6w",
    "jupyter": {
     "outputs_hidden": true
    },
    "outputId": "4f01d0c0-de3f-44b2-df05-b7c734f15448"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at facebook/wav2vec2-xls-r-1b were not used when initializing Wav2Vec2ForCTC: ['project_q.weight', 'project_hid.weight', 'project_hid.bias', 'project_q.bias', 'quantizer.weight_proj.bias', 'quantizer.codevectors', 'quantizer.weight_proj.weight']\n",
      "- This IS expected if you are initializing Wav2Vec2ForCTC 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 Wav2Vec2ForCTC 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 Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/wav2vec2-xls-r-1b and are newly initialized: ['lm_head.weight', 'lm_head.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Wav2Vec2ForCTC(\n",
       "  (wav2vec2): Wav2Vec2Model(\n",
       "    (feature_extractor): Wav2Vec2FeatureEncoder(\n",
       "      (conv_layers): ModuleList(\n",
       "        (0): Wav2Vec2LayerNormConvLayer(\n",
       "          (conv): Conv1d(1, 512, kernel_size=(10,), stride=(5,))\n",
       "          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (1): Wav2Vec2LayerNormConvLayer(\n",
       "          (conv): Conv1d(512, 512, kernel_size=(3,), stride=(2,))\n",
       "          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (2): Wav2Vec2LayerNormConvLayer(\n",
       "          (conv): Conv1d(512, 512, kernel_size=(3,), stride=(2,))\n",
       "          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (3): Wav2Vec2LayerNormConvLayer(\n",
       "          (conv): Conv1d(512, 512, kernel_size=(3,), stride=(2,))\n",
       "          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (4): Wav2Vec2LayerNormConvLayer(\n",
       "          (conv): Conv1d(512, 512, kernel_size=(3,), stride=(2,))\n",
       "          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (5): Wav2Vec2LayerNormConvLayer(\n",
       "          (conv): Conv1d(512, 512, kernel_size=(2,), stride=(2,))\n",
       "          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (6): Wav2Vec2LayerNormConvLayer(\n",
       "          (conv): Conv1d(512, 512, kernel_size=(2,), stride=(2,))\n",
       "          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (feature_projection): Wav2Vec2FeatureProjection(\n",
       "      (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "      (projection): Linear(in_features=512, out_features=1280, bias=True)\n",
       "      (dropout): Dropout(p=0.04, inplace=False)\n",
       "    )\n",
       "    (encoder): Wav2Vec2EncoderStableLayerNorm(\n",
       "      (pos_conv_embed): Wav2Vec2PositionalConvEmbedding(\n",
       "        (conv): Conv1d(1280, 1280, kernel_size=(128,), stride=(1,), padding=(64,), groups=16)\n",
       "        (padding): Wav2Vec2SamePadLayer()\n",
       "      )\n",
       "      (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "      (dropout): Dropout(p=0.047, inplace=False)\n",
       "      (layers): ModuleList(\n",
       "        (0): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (1): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (2): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (3): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (4): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (5): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (6): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (7): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (8): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (9): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (10): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (11): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (12): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (13): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (14): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (15): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (16): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (17): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (18): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (19): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (20): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (21): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (22): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (23): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (24): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (25): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (26): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (27): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (28): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (29): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (30): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (31): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (32): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (33): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (34): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (35): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (36): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (37): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (38): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (39): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (40): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (41): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (42): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (43): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (44): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (45): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (46): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (47): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (dropout): Dropout(p=0.0, inplace=False)\n",
       "  (lm_head): Linear(in_features=1280, out_features=36, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from transformers import Wav2Vec2ForCTC\n",
    "\n",
    "model = Wav2Vec2ForCTC.from_pretrained(\n",
    "    \"facebook/wav2vec2-xls-r-1b\", \n",
    "    attention_dropout=0.094,\n",
    "    hidden_dropout=0.047,\n",
    "    feat_proj_dropout=0.04,\n",
    "    mask_time_prob=0.082,\n",
    "    layerdrop=0.041,\n",
    "    activation_dropout=0.055,\n",
    "    ctc_loss_reduction=\"mean\", \n",
    "    pad_token_id=processor.tokenizer.pad_token_id,\n",
    "    vocab_size=len(processor.tokenizer),\n",
    ")\n",
    "model.to('cuda')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1DwR3XLSzGDD"
   },
   "source": [
    "The first component of XLS-R consists of a stack of CNN layers that are used to extract acoustically meaningful - but contextually independent - features from the raw speech signal. This part of the model has already been sufficiently trained during pretraining and as stated in the [paper](https://arxiv.org/pdf/2006.13979.pdf) does not need to be fine-tuned anymore. \n",
    "Thus, we can set the `requires_grad` to `False` for all parameters of the *feature extraction* part."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "oGI8zObtZ3V0"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:1680: FutureWarning: The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5.Please use the equivalent `freeze_feature_encoder` method instead.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "model.freeze_feature_extractor()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lD4aGhQM0K-D"
   },
   "source": [
    "In a final step, we define all parameters related to training. \n",
    "To give more explanation on some of the parameters:\n",
    "- `group_by_length` makes training more efficient by grouping training samples of similar input length into one batch. This can significantly speed up training time by heavily reducing the overall number of useless padding tokens that are passed through the model\n",
    "- `learning_rate` and `weight_decay` were heuristically tuned until fine-tuning has become stable. Note that those parameters strongly depend on the Common Voice dataset and might be suboptimal for other speech datasets.\n",
    "\n",
    "For more explanations on other parameters, one can take a look at the [docs](https://huggingface.co/transformers/master/main_classes/trainer.html?highlight=trainer#trainingarguments).\n",
    "\n",
    "During training, a checkpoint will be uploaded asynchronously to the hub every 400 training steps. It allows you to also play around with the demo widget even while your model is still training.\n",
    "\n",
    "**Note**: If one does not want to upload the model checkpoints to the hub, simply set `push_to_hub=False`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ[\"WANDB_DISABLED\"] = \"true\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "id": "KbeKSV7uzGPP"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using the `WAND_DISABLED` environment variable is deprecated and will be removed in v5. Use the --report_to flag to control the integrations used for logging result (for instance --report_to none).\n"
     ]
    }
   ],
   "source": [
    "from transformers import TrainingArguments\n",
    "\n",
    "training_args = TrainingArguments(\n",
    "  output_dir=repo_name,\n",
    "  group_by_length=True,\n",
    "  per_device_train_batch_size=8,\n",
    "  gradient_accumulation_steps=2,\n",
    "  evaluation_strategy=\"steps\",\n",
    "  num_train_epochs=4,\n",
    "  gradient_checkpointing=True,\n",
    "  fp16=True,\n",
    "  save_steps=400,\n",
    "  eval_steps=400,\n",
    "  logging_steps=50,\n",
    "  learning_rate=1e-4,\n",
    "  warmup_steps=100,\n",
    "  save_total_limit=3,\n",
    "  push_to_hub=True,\n",
    "  load_best_model_at_end=True,\n",
    "  greater_is_better=False,\n",
    "  metric_for_best_model='eval_wer',\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OsW-WZcL1ZtN"
   },
   "source": [
    "Now, all instances can be passed to Trainer and we are ready to start training!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "id": "rY7vBmFCPFgC",
    "outputId": "441e7019-a5a1-4c0a-afa2-cced90ffed05"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/workspace/xlsr_fi/wav2vec2-xlsr-fi-train-aug-lm-1B is already a clone of https://huggingface.co/RASMUS/wav2vec2-xlsr-fi-train-aug-lm-1B. Make sure you pull the latest changes with `repo.git_pull()`.\n",
      "Using amp half precision backend\n"
     ]
    }
   ],
   "source": [
    "from transformers import Trainer\n",
    "\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    data_collator=data_collator,\n",
    "    args=training_args,\n",
    "    compute_metrics=compute_metrics,\n",
    "    train_dataset=common_voice_train_audio_augmented,\n",
    "    eval_dataset=common_voice_test_audio,\n",
    "    tokenizer=processor.feature_extractor,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UoXBx1JAA0DX"
   },
   "source": [
    "\n",
    "\n",
    "---\n",
    "\n",
    "${}^1$ To allow models to become independent of the speaker rate, in CTC, consecutive tokens that are identical are simply grouped as a single token. However, the encoded labels should not be grouped when decoding since they don't correspond to the predicted tokens of the model, which is why the `group_tokens=False` parameter has to be passed. If we wouldn't pass this parameter a word like `\"hello\"` would incorrectly be encoded, and decoded as `\"helo\"`.\n",
    "\n",
    "${}^2$ The blank token allows the model to predict a word, such as `\"hello\"` by forcing it to insert the blank token between the two l's. A CTC-conform prediction of `\"hello\"` of our model would be `[PAD] [PAD] \"h\" \"e\" \"e\" \"l\" \"l\" [PAD] \"l\" \"o\" \"o\" [PAD]`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rpvZHM1xReIW"
   },
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "j-3oKSzZ1hGq"
   },
   "source": [
    "Training will take multiple hours depending on the GPU allocated to this notebook. While the trained model yields somewhat satisfying results on *Common Voice*'s test data of Turkish, it is by no means an optimally fine-tuned model. The purpose of this notebook is just to demonstrate how to fine-tune XLS-R on an ASR dataset.\n",
    "\n",
    "In case you want to use this google colab to fine-tune your model, you should make sure that your training doesn't stop due to inactivity. A simple hack to prevent this is to paste the following code into the console of this tab (*right mouse click -> inspect -> Console tab and insert code*)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VYYAvgkW4P0m"
   },
   "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",
   "metadata": {
    "id": "7bGgLV2r0yvZ"
   },
   "source": [
    "Depending on what GPU was allocated to your google colab it might be possible that you are seeing an `\"out-of-memory\"` error here. In this case, it's probably best to reduce `per_device_train_batch_size` to 8 or even less and increase [`gradient_accumulation`](https://huggingface.co/transformers/master/main_classes/trainer.html#trainingarguments)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "id": "9fRr9TG5pGBl",
    "outputId": "e07eeffc-bbd6-4f1d-a45e-b74bad290f79"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The following columns in the training set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "/opt/conda/lib/python3.8/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use thePyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  warnings.warn(\n",
      "***** Running training *****\n",
      "  Num examples = 22174\n",
      "  Num Epochs = 4\n",
      "  Instantaneous batch size per device = 8\n",
      "  Total train batch size (w. parallel, distributed & accumulation) = 16\n",
      "  Gradient Accumulation steps = 2\n",
      "  Total optimization steps = 5544\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='5544' max='5544' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [5544/5544 4:21:28, Epoch 4/4]\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",
       "      <th>Wer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>400</td>\n",
       "      <td>0.647300</td>\n",
       "      <td>0.285699</td>\n",
       "      <td>0.382526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>800</td>\n",
       "      <td>0.603900</td>\n",
       "      <td>0.245895</td>\n",
       "      <td>0.347557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1200</td>\n",
       "      <td>0.475700</td>\n",
       "      <td>0.233797</td>\n",
       "      <td>0.327387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1600</td>\n",
       "      <td>0.447300</td>\n",
       "      <td>0.224628</td>\n",
       "      <td>0.312791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2000</td>\n",
       "      <td>0.432200</td>\n",
       "      <td>0.196223</td>\n",
       "      <td>0.280458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2400</td>\n",
       "      <td>0.396100</td>\n",
       "      <td>0.206987</td>\n",
       "      <td>0.279749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2800</td>\n",
       "      <td>0.364200</td>\n",
       "      <td>0.178981</td>\n",
       "      <td>0.247314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3200</td>\n",
       "      <td>0.356100</td>\n",
       "      <td>0.176911</td>\n",
       "      <td>0.237482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3600</td>\n",
       "      <td>0.282000</td>\n",
       "      <td>0.167229</td>\n",
       "      <td>0.226333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4000</td>\n",
       "      <td>0.297800</td>\n",
       "      <td>0.163568</td>\n",
       "      <td>0.219238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4400</td>\n",
       "      <td>0.272200</td>\n",
       "      <td>0.163688</td>\n",
       "      <td>0.210217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4800</td>\n",
       "      <td>0.292400</td>\n",
       "      <td>0.150580</td>\n",
       "      <td>0.202108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5200</td>\n",
       "      <td>0.263100</td>\n",
       "      <td>0.149869</td>\n",
       "      <td>0.195520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 1595\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-400\n",
      "Configuration saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-400/config.json\n",
      "Model weights saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-400/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-400/preprocessor_config.json\n",
      "Configuration saved in wav2vec2-xlsr-fi-train-aug-lm-1B/preprocessor_config.json\n",
      "Several commits (2) will be pushed upstream.\n",
      "Deleting older checkpoint [wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-7600] due to args.save_total_limit\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 1595\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-800\n",
      "Configuration saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-800/config.json\n",
      "Model weights saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-800/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-800/preprocessor_config.json\n",
      "Configuration saved in wav2vec2-xlsr-fi-train-aug-lm-1B/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-8000] due to args.save_total_limit\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 1595\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-1200\n",
      "Configuration saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-1200/config.json\n",
      "Model weights saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-1200/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-1200/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-8400] due to args.save_total_limit\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 1595\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-1600\n",
      "Configuration saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-1600/config.json\n",
      "Model weights saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-1600/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-1600/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-400] due to args.save_total_limit\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 1595\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-2000\n",
      "Configuration saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-2000/config.json\n",
      "Model weights saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-2000/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-2000/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-800] due to args.save_total_limit\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 1595\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-2400\n",
      "Configuration saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-2400/config.json\n",
      "Model weights saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-2400/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-2400/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-1200] due to args.save_total_limit\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 1595\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-2800\n",
      "Configuration saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-2800/config.json\n",
      "Model weights saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-2800/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-2800/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-1600] due to args.save_total_limit\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 1595\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-3200\n",
      "Configuration saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-3200/config.json\n",
      "Model weights saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-3200/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-3200/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-2000] due to args.save_total_limit\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 1595\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-3600\n",
      "Configuration saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-3600/config.json\n",
      "Model weights saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-3600/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-3600/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-2400] due to args.save_total_limit\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 1595\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-4000\n",
      "Configuration saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-4000/config.json\n",
      "Model weights saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-4000/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-4000/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-2800] due to args.save_total_limit\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 1595\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-4400\n",
      "Configuration saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-4400/config.json\n",
      "Model weights saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-4400/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-4400/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-3200] due to args.save_total_limit\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 1595\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-4800\n",
      "Configuration saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-4800/config.json\n",
      "Model weights saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-4800/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-4800/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-3600] due to args.save_total_limit\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 1595\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-5200\n",
      "Configuration saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-5200/config.json\n",
      "Model weights saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-5200/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-5200/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-4000] due to args.save_total_limit\n",
      "\n",
      "\n",
      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
      "\n",
      "\n",
      "Loading best model from wav2vec2-xlsr-fi-train-aug-lm-1B/checkpoint-5200 (score: 0.19551996756537604).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=5544, training_loss=0.3942209981048606, metrics={'train_runtime': 15698.6908, 'train_samples_per_second': 5.65, 'train_steps_per_second': 0.353, 'total_flos': 5.82002597856266e+19, 'train_loss': 0.3942209981048606, 'epoch': 4.0})"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a9q4mgMZplr_"
   },
   "source": [
    "The training loss and validation WER go down nicely."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4Ya7WEy0pd13"
   },
   "source": [
    "You can now upload the result of the training to the 🤗 Hub, just execute this instruction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "id": "ArG1Thf6NBWm",
    "outputId": "62ef1c3d-786c-4e25-f9c5-4020e71aa298"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving model checkpoint to wav2vec2-xlsr-fi-train-aug-lm-1B\n",
      "Configuration saved in wav2vec2-xlsr-fi-train-aug-lm-1B/config.json\n",
      "Model weights saved in wav2vec2-xlsr-fi-train-aug-lm-1B/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-fi-train-aug-lm-1B/preprocessor_config.json\n",
      "Several commits (2) will be pushed upstream.\n",
      "The progress bars may be unreliable.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b59f278120294e64a1192c8a6ec7c84d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Upload file pytorch_model.bin:   0%|          | 3.37k/3.59G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "To https://huggingface.co/RASMUS/wav2vec2-xlsr-fi-train-aug-lm-1B\n",
      "   53621f5..ddf9cad  main -> main\n",
      "\n",
      "Dropping the following result as it does not have all the necessary fields:\n",
      "{}\n",
      "To https://huggingface.co/RASMUS/wav2vec2-xlsr-fi-train-aug-lm-1B\n",
      "   ddf9cad..e13053b  main -> main\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'https://huggingface.co/RASMUS/wav2vec2-xlsr-fi-train-aug-lm-1B/commit/ddf9cadee318e2732e136ec4aa2789c0de8e06fa'"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.push_to_hub()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RHIVc44_fY2N"
   },
   "source": [
    "You can now share this model with all your friends, family, favorite pets: they can all load it with the identifier \"your-username/the-name-you-picked\" so for instance:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5lWWIKyBpx1h"
   },
   "source": [
    "```python\n",
    "from transformers import AutoModelForCTC, Wav2Vec2Processor\n",
    "\n",
    "model = AutoModelForCTC.from_pretrained(\"patrickvonplaten/wav2vec2-large-xls-r-300m-tr-colab\")\n",
    "processor = Wav2Vec2Processor.from_pretrained(\"patrickvonplaten/wav2vec2-large-xls-r-300m-tr-colab\")\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pmi1cX0fRBit"
   },
   "source": [
    "For more examples of how XLS-R can be fine-tuned, please take a look at the [official speech recognition examples](https://github.com/huggingface/transformers/tree/master/examples/pytorch/speech-recognition#examples)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "L8b8Qkoy3KyS"
   },
   "source": [
    "### Evaluation\n",
    "\n",
    "As a final check, let's load the model and verify that it indeed has learned to transcribe Turkish speech.\n",
    "\n",
    "Let's first load the pretrained checkpoint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true,
    "id": "R351I9IQp_9D",
    "jupyter": {
     "outputs_hidden": true
    },
    "outputId": "f2a2ee99-7db6-4962-e140-0107054102d3"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading configuration file wav2vec2-xlsr-fi-lm-1B/config.json\n",
      "Model config Wav2Vec2Config {\n",
      "  \"_name_or_path\": \"facebook/wav2vec2-xls-r-1b\",\n",
      "  \"activation_dropout\": 0.055,\n",
      "  \"adapter_kernel_size\": 3,\n",
      "  \"adapter_stride\": 2,\n",
      "  \"add_adapter\": false,\n",
      "  \"apply_spec_augment\": true,\n",
      "  \"architectures\": [\n",
      "    \"Wav2Vec2ForCTC\"\n",
      "  ],\n",
      "  \"attention_dropout\": 0.094,\n",
      "  \"bos_token_id\": 1,\n",
      "  \"classifier_proj_size\": 256,\n",
      "  \"codevector_dim\": 1024,\n",
      "  \"contrastive_logits_temperature\": 0.1,\n",
      "  \"conv_bias\": true,\n",
      "  \"conv_dim\": [\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512\n",
      "  ],\n",
      "  \"conv_kernel\": [\n",
      "    10,\n",
      "    3,\n",
      "    3,\n",
      "    3,\n",
      "    3,\n",
      "    2,\n",
      "    2\n",
      "  ],\n",
      "  \"conv_stride\": [\n",
      "    5,\n",
      "    2,\n",
      "    2,\n",
      "    2,\n",
      "    2,\n",
      "    2,\n",
      "    2\n",
      "  ],\n",
      "  \"ctc_loss_reduction\": \"mean\",\n",
      "  \"ctc_zero_infinity\": false,\n",
      "  \"diversity_loss_weight\": 0.1,\n",
      "  \"do_stable_layer_norm\": true,\n",
      "  \"eos_token_id\": 2,\n",
      "  \"feat_extract_activation\": \"gelu\",\n",
      "  \"feat_extract_dropout\": 0.0,\n",
      "  \"feat_extract_norm\": \"layer\",\n",
      "  \"feat_proj_dropout\": 0.04,\n",
      "  \"feat_quantizer_dropout\": 0.0,\n",
      "  \"final_dropout\": 0.0,\n",
      "  \"gradient_checkpointing\": false,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout\": 0.047,\n",
      "  \"hidden_size\": 1280,\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 5120,\n",
      "  \"layer_norm_eps\": 1e-05,\n",
      "  \"layerdrop\": 0.041,\n",
      "  \"mask_feature_length\": 10,\n",
      "  \"mask_feature_min_masks\": 0,\n",
      "  \"mask_feature_prob\": 0.0,\n",
      "  \"mask_time_length\": 10,\n",
      "  \"mask_time_min_masks\": 2,\n",
      "  \"mask_time_prob\": 0.082,\n",
      "  \"model_type\": \"wav2vec2\",\n",
      "  \"num_adapter_layers\": 3,\n",
      "  \"num_attention_heads\": 16,\n",
      "  \"num_codevector_groups\": 2,\n",
      "  \"num_codevectors_per_group\": 320,\n",
      "  \"num_conv_pos_embedding_groups\": 16,\n",
      "  \"num_conv_pos_embeddings\": 128,\n",
      "  \"num_feat_extract_layers\": 7,\n",
      "  \"num_hidden_layers\": 48,\n",
      "  \"num_negatives\": 100,\n",
      "  \"output_hidden_size\": 1280,\n",
      "  \"pad_token_id\": 33,\n",
      "  \"proj_codevector_dim\": 1024,\n",
      "  \"tdnn_dilation\": [\n",
      "    1,\n",
      "    2,\n",
      "    3,\n",
      "    1,\n",
      "    1\n",
      "  ],\n",
      "  \"tdnn_dim\": [\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    512,\n",
      "    1500\n",
      "  ],\n",
      "  \"tdnn_kernel\": [\n",
      "    5,\n",
      "    3,\n",
      "    3,\n",
      "    1,\n",
      "    1\n",
      "  ],\n",
      "  \"torch_dtype\": \"float32\",\n",
      "  \"transformers_version\": \"4.16.0.dev0\",\n",
      "  \"use_weighted_layer_sum\": false,\n",
      "  \"vocab_size\": 36,\n",
      "  \"xvector_output_dim\": 512\n",
      "}\n",
      "\n",
      "loading weights file wav2vec2-xlsr-fi-lm-1B/pytorch_model.bin\n",
      "All model checkpoint weights were used when initializing Wav2Vec2ForCTC.\n",
      "\n",
      "All the weights of Wav2Vec2ForCTC were initialized from the model checkpoint at wav2vec2-xlsr-fi-lm-1B.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use Wav2Vec2ForCTC for predictions without further training.\n",
      "loading feature extractor configuration file wav2vec2-xlsr-fi-lm-1B/preprocessor_config.json\n",
      "Feature extractor Wav2Vec2FeatureExtractor {\n",
      "  \"do_normalize\": true,\n",
      "  \"feature_extractor_type\": \"Wav2Vec2FeatureExtractor\",\n",
      "  \"feature_size\": 1,\n",
      "  \"padding_side\": \"right\",\n",
      "  \"padding_value\": 0.0,\n",
      "  \"return_attention_mask\": true,\n",
      "  \"sampling_rate\": 16000\n",
      "}\n",
      "\n",
      "Didn't find file wav2vec2-xlsr-fi-lm-1B/tokenizer.json. We won't load it.\n",
      "loading file wav2vec2-xlsr-fi-lm-1B/vocab.json\n",
      "loading file wav2vec2-xlsr-fi-lm-1B/tokenizer_config.json\n",
      "loading file wav2vec2-xlsr-fi-lm-1B/added_tokens.json\n",
      "loading file wav2vec2-xlsr-fi-lm-1B/special_tokens_map.json\n",
      "loading file None\n",
      "Adding <s> to the vocabulary\n",
      "Adding </s> to the vocabulary\n"
     ]
    }
   ],
   "source": [
    "model = Wav2Vec2ForCTC.from_pretrained(repo_name).to(\"cuda\")\n",
    "processor = Wav2Vec2Processor.from_pretrained(repo_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jD7TZ1YS3S_K"
   },
   "source": [
    "\n",
    "Now, we will just take the first example of the test set, run it through the model and take the `argmax(...)` of the logits to retrieve the predicted token ids."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "id": "pax07TnL3WZn",
    "outputId": "867787ff-0cb7-41e9-f926-96f7b53e7134"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "It is strongly recommended to pass the ``sampling_rate`` argument to this function. Failing to do so can result in silent errors that might be hard to debug.\n"
     ]
    }
   ],
   "source": [
    "input_dict = processor(common_voice_test_audio[0][\"input_values\"], return_tensors=\"pt\", padding=True)\n",
    "\n",
    "logits = model(input_dict.input_values.to(\"cuda\")).logits\n",
    "\n",
    "pred_ids = torch.argmax(logits, dim=-1)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7nkzSQu53Zs2"
   },
   "source": [
    "We adapted `common_voice_test` quite a bit so that the dataset instance does not contain the original sentence label anymore. Thus, we re-use the original dataset to get the label of the first example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "fe2AE-2xqKHx",
    "outputId": "1d8321b3-4f41-4d71-e74e-f33f32a7b261"
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "Downloading:   0%|          | 0.00/4.62k [00:00<?, ?B/s]"
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     "metadata": {},
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using custom data configuration fi-00b7e43d66b8c1a3\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset common_voice/fi (download: 47.57 MiB, generated: 53.09 MiB, post-processed: Unknown size, total: 100.66 MiB) to /workspace/.cache/huggingface/datasets/common_voice/fi-00b7e43d66b8c1a3/6.1.0/5693bfc0feeade582a78c2fb250bc88f52bd86f0a7f1bb22bfee67e715de30fd...\n"
     ]
    },
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      "text/plain": [
       "Downloading:   0%|          | 0.00/49.9M [00:00<?, ?B/s]"
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     },
     "metadata": {},
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    },
    {
     "data": {
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       "model_id": "",
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       "version_minor": 0
      },
      "text/plain": [
       "0 examples [00:00, ? examples/s]"
      ]
     },
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    },
    {
     "data": {
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       "0 examples [00:00, ? examples/s]"
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       "0 examples [00:00, ? examples/s]"
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    },
    {
     "data": {
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       "0 examples [00:00, ? examples/s]"
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     },
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    {
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset common_voice downloaded and prepared to /workspace/.cache/huggingface/datasets/common_voice/fi-00b7e43d66b8c1a3/6.1.0/5693bfc0feeade582a78c2fb250bc88f52bd86f0a7f1bb22bfee67e715de30fd. Subsequent calls will reuse this data.\n"
     ]
    }
   ],
   "source": [
    "common_voice_test_transcription = load_dataset(\"common_voice\", \"fi\", data_dir=\"./cv-corpus-6.1-2020-12-11\", split=\"test\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "epu8kCQZ3h70"
   },
   "source": [
    "\n",
    "Finally, we can decode the example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "id": "K4xWqmk_qMn0",
    "outputId": "d9e40b3c-f02a-48a1-d081-6d7e8b37dcaf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction:\n",
      "nyt en misään tapauksessa sinula auttaa\n",
      "\n",
      "Reference:\n",
      "tiibetinspanielit olivat luostareiden ja ylhäisön arvostettuja seurakoiria\n"
     ]
    }
   ],
   "source": [
    "print(\"Prediction:\")\n",
    "print(processor.decode(pred_ids))\n",
    "\n",
    "print(\"\\nReference:\")\n",
    "print(common_voice_test_transcription[0][\"sentence\"].lower())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HwhyoMml3oOT"
   },
   "source": [
    "Alright! The transcription can definitely be recognized from our prediction, but it is not perfect yet. Training the model a bit longer, spending more time on the data preprocessing, and especially using a language model for decoding would certainly improve the model's overall performance.\n",
    "\n",
    "For a demonstration model on a low-resource language, the results are quite acceptable however 🤗."
   ]
  }
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