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null, "flex_flow": null, "width": null, "min_width": null, "border": null, "align_items": null, "bottom": null, "_model_module": "@jupyter-widgets/base", "top": null, "grid_column": null, "overflow_y": null, "overflow_x": null, "grid_auto_flow": null, "grid_area": null, "grid_template_columns": null, "flex": null, "_model_name": "LayoutModel", "justify_items": null, "grid_row": null, "max_height": null, "align_content": null, "visibility": null, "align_self": null, "height": null, "min_height": null, "padding": null, "grid_auto_rows": null, "grid_gap": null, "max_width": null, "order": null, "_view_module_version": "1.2.0", "grid_template_areas": null, "object_position": null, "object_fit": null, "grid_auto_columns": null, "margin": null, "display": null, "left": null } }, "9d6447b1a5ba435681b6d0b5152a2324": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_view_name": "StyleView", "_model_name": 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"align_content": null, "visibility": null, "align_self": null, "height": null, "min_height": null, "padding": null, "grid_auto_rows": null, "grid_gap": null, "max_width": null, "order": null, "_view_module_version": "1.2.0", "grid_template_areas": null, "object_position": null, "object_fit": null, "grid_auto_columns": null, "margin": null, "display": null, "left": null } } } }, "accelerator": "GPU" }, "cells": [ { "cell_type": "code", "source": [ "!nvidia-smi" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "M2b-7B0PjEvG", "outputId": "30e8d5a6-10f6-46e6-b231-57972180ee86" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Sun Jan 30 18:06:30 2022 \n", "+-----------------------------------------------------------------------------+\n", "| NVIDIA-SMI 495.46 Driver Version: 460.32.03 CUDA Version: 11.2 |\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 P100-PCIE... Off | 00000000:00:04.0 Off | 0 |\n", "| N/A 40C P0 30W / 250W | 0MiB / 16280MiB | 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" ] } ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "YZXTLpRzg8gp" }, "outputs": [], "source": [ "%%capture\n", "!pip install datasets\n", "!pip install transformers\n", "!pip install huggingface_hub\n", "!pip install torchaudio\n", "!pip install librosa\n", "!pip install jiwer" ] }, { "cell_type": "code", "source": [ "!apt install git-lfs" ], "metadata": { "id": "k6Ao2XfngKmI", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "5e3d4c99-5dfe-4e43-ccb5-76e7bdd019b5" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Reading package lists... Done\n", "Building dependency tree \n", "Reading state information... Done\n", "The following NEW packages will be installed:\n", " git-lfs\n", "0 upgraded, 1 newly installed, 0 to remove and 37 not upgraded.\n", "Need to get 2,129 kB of archives.\n", "After this operation, 7,662 kB of additional disk space will be used.\n", "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 git-lfs amd64 2.3.4-1 [2,129 kB]\n", "Fetched 2,129 kB in 2s (928 kB/s)\n", "Selecting previously unselected package git-lfs.\n", "(Reading database ... 155229 files and directories currently installed.)\n", "Preparing to unpack .../git-lfs_2.3.4-1_amd64.deb ...\n", "Unpacking git-lfs (2.3.4-1) ...\n", "Setting up git-lfs (2.3.4-1) ...\n", "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n" ] } ] }, { "cell_type": "code", "source": [ "from huggingface_hub import notebook_login\n", "\n", "notebook_login()" ], "metadata": { "id": "1CCVxM2-hLbE", "colab": { "base_uri": "https://localhost:8080/", "height": 359, "referenced_widgets": [ "39e1693dfe364c898f855a0706278262", "93228b298bb74a1f85341d728f9a05e4", "20042b9ba890434ca76d853f95e4ffe7", "4954c5e13785448ea4821396c9f1b466", "e2f1953e779148e09ea69d5ec4dc847a", "ba52e726328847b08bbbf1c30e59c9fd", "c37068c8f8e74da18bec74dcb7451d66", "fc19041cb6544158b92e4f322c33ae44", "f134c5e6f443443babb01556ff0facbc", "f4eddb2180a245f8ac3b7d4b7ccb83f6", "3b237748c2344b8c8cd7dec486f5043e", "f98fde11c94d4128a1ee24cec5b2c339", "379dccd4e0af4f2ca6971ef7970f20fd", "276b07821d4f4b4594e485237b920c92", "62424b7d68124d94b232eaaab9601ff9", "984d56a0e4cd4f089af687f2769f0f45", "6a913847fd0e4369b2459df029614b23" ] }, "outputId": "2441b8a3-6e54-424c-b564-835fef5754e7" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Login successful\n", "Your token has been saved to /root/.huggingface/token\n", "\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n", "You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n", "\n", "git config --global credential.helper store\u001b[0m\n" ] } ] }, { "cell_type": "code", "source": [ "from datasets import load_dataset, load_metric, Audio" ], "metadata": { "id": "HP3z-kMqhW49" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "!sudo rm -r /root/.cache" ], "metadata": { "id": "OvVBJh9C5x_l" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "common_voice_train = load_dataset(\"mozilla-foundation/common_voice_8_0\", \"ha\", split=\"train+validation\", use_auth_token=True)\n", "common_voice_test = load_dataset(\"mozilla-foundation/common_voice_8_0\", \"ha\", split=\"test\", use_auth_token=True)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 377, "referenced_widgets": [ "c2a6609c04b04e8d827133334ccf81a2", "b8d55a4ae7884696a7b495eefeca3f67", 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"8f63f7fbf2984750832fbaa26eab153d", "74ece77626c948c4ae0605b5acf2209b", "d7ba02e983c74066a9a0ae78ea6247c2", "1c761a062c764785bc7da8857b014e19", "48d06f2ad64a4983b55411ee7bb5e5d0", "1f2f20d4cf954fd893d959bba080834f", "12ad97f96f3e47e38abde7decefe4992", "b3e257f3aea248229d01c439528721ad", "07dbb6fc72b64451ab9353eb3b2df7c9", "a2468a1b30e54ff9bea3b4092853ec43", "14424281b5cc4fd7af756bff0989ccff", "929db4a1ad15475f962540e1f5ab800c", "c35af583866d433f93a1170b31b09f6b", "cdd2e2b75f644a0d82a611d57e4bf216", "b746660561d64a77a93fbe0b76b2f5ce", "8f62c7ccc04843e89060db77a7be4526", "d9989af910a34448b81d3d0b83be98a8", "7c7ce4cef7a747f8b1eb59a5cc2ae334", "bc88c922d4434422b1e89c8bff9d62c9", "d31f57c0148e490f96f3f0a4ee7bee22", "1a07a8cdc2c74fecab4b70af03d73a88", "1474a83d30fd4047abf6cf449ab7fd32", "4a7b7df4bb2a4150a64564187ca12b59", "d46b60138a19421e9f6c0a8e9f168549", "cb084c00ccc24eca97373470445ed496", "7cc4b08b2f504015b0465a7cba8b54e8", "51bd122c6d254fb4af7abb0e538a6fa1", "186f517051f9415483cb27da0a139db6", "0dbbdaa9ce104579b6f1206e95c69702", "d7165e4ffa3040cdb5f02c1b67b90d49", "6937d752ed3c4f548e609c21d1b23524", "d1aa741e5e404927a38794fc0f477959", "707da26da992470f8fc8839bc2bfe3c9", "7fd69e5a7873456b8301b505d99a57d3", "53d3d6d0d7d84f2e9d867c56b2059a1f", "40f096cdc2db4a31b15a32b01a99558c", "55b5d0518dc94296bf30e6531f109620", "2402388de6cb458ebf1c3f547f8eb9e9", "5ec1f4b52361406297857d2a9fa9c88a", "0ffba31afd2247b2897b6b641ef5c565", "200615050f0d4f77b9a8db87b5e79828", "cdf68fc559c04120858e8a9a1bb95b60", "dbe8b615cf22489aa389653ab338f011", "f441214e460e48be8d19aeb67de13c22", "c0e4b7d0eccd4a16a0d9575bfd7d27f8", "8a60a17e80364a4f8ba5fb67b6ed37be", "ce60582841c348c28ce97a46c12bfb0f", "1a646e23eee24f71ac8dbcef1776e8be", "1260a8c158284d76870682ffb28e0191", "390bfa1bc430443f94d1775fe3534c29", "e199c3fd19b94ac7bb8c59b5efe01ff7", "306ab39347064da58878fd1e1131bddb", "3a992683baf446dd9f49fa0bc582abfe", "1e1a5c83c3014ed9a787a1f58717717a", "9dc7b748277e4e868ef56c9f017f5e6f", "21ce97998c30482689936d37d6eee4cb", "e0cf597caf894190b1627502c79e5aff", "3b84f16bd8e0435f91743dc218cd320b", "ec1ae1cf60d44343992908b606500b5c", "ad53ad9be8fc4078b5de8ab87d0241a9", "ea8db7f21cab4c91b7dc7947075e347a", "000bcf0674a14065aedae6f7a696369f", "3573d7dc45144df28a0ebc74090420d8", "ea01665ced9748e499211fd1523bf000", "f0a16554bc024808bf6ae630da486eec", "eff2ed8a368140a58c174ceb1e69e563", "fb81359cc21a48a19abea42888e6f8c4", "8865005ef8f94f30b4874608617a7d81", "cacb0214e531475d91a4c22a1602e679", "cd9971a455694057bb3e29ac2acb621a" ] }, "id": "hnwqYEFnhji5", "outputId": "31e3bebe-37e9-46ad-e04b-b9d3180c8970" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c2a6609c04b04e8d827133334ccf81a2", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Downloading: 0%| | 0.00/10.1k [00:00 main\n", "\n" ] }, { "output_type": "execute_result", "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'https://huggingface.co/anuragshas/wav2vec2-large-xls-r-300m-ha-cv8/commit/64ff0d9d37d20d2363890c287a646dd76cd7bc50'" ] }, "metadata": {}, "execution_count": 19 } ] }, { "cell_type": "code", "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)" ], "metadata": { "id": "1RseFfmleI3H" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from transformers import Wav2Vec2Processor\n", "\n", "processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)" ], "metadata": { "id": "NGDrT6saeOqy" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "common_voice_train = common_voice_train.cast_column(\"audio\", Audio(sampling_rate=16_000))\n", "common_voice_test = common_voice_test.cast_column(\"audio\", Audio(sampling_rate=16_000))" ], "metadata": { "id": "nOpdod1weRfa" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "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" ], "metadata": { "id": "KimgJI22eVYa" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "common_voice_train = common_voice_train.map(prepare_dataset, remove_columns=common_voice_train.column_names)\n", "common_voice_test = common_voice_test.map(prepare_dataset, remove_columns=common_voice_test.column_names)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "7df77ec2e4434555bd625c88142624e5", "dc29a68593a147fd9f7f197c6cf031f9", "d8d90c6766c042e4ad95f7ebe65df9de", "20502dcc8ab54fb28d66a12e59e460bc", "782cea77e09c42d7841e6cb9f4d6ae4a", "1c2f484100f44ef692ac60ea37ae795b", "c1a9a4bb0183427d9d05500d293037c0", "e8783d49b6264c2898088f3b01a1353d", "ebef0bfa18aa4051a522b5f82e83b63d", "771db2d95d5d496f9e4213cd03bdc83b", "031d8484ee954cd7ad8020de6eace0b3", "e30a930b820f4610acd41d2442b6cbfb", "c61f0600f8144af89ff61a9ab4d8d58a", "454c0a32562045c581236a327f7412de", "3c2939ae8faa4303af7a5776ab1954d8", "3876cc6f948843f8af7b7581801b253b", "b4ff66868c6b4fe4a18a070a73ac54f8", "dc16e0e94f1e4f9991389436cd80d68b", "c922637427ee4b8681c541c12544ebe6", "50b316dc7ab94ce4b405a92c45c57ae4", "a55bf8ee695a4b8bb55f1cc3c96d3936", "3d93eb28e2e943a791432a3affaa1bc6" ] }, "id": "Uxt4G7XweZxf", "outputId": "d98d6a8e-4a50-4ba5-9a3e-5f2f00e51d16" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7df77ec2e4434555bd625c88142624e5", "version_minor": 0, "version_major": 2 }, "text/plain": [ "0ex [00:00, ?ex/s]" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e30a930b820f4610acd41d2442b6cbfb", "version_minor": 0, "version_major": 2 }, "text/plain": [ "0ex [00:00, ?ex/s]" ] }, "metadata": {} } ] }, { "cell_type": "code", "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" ], "metadata": { "id": "k1hUL21-ecSE" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)" ], "metadata": { "id": "3WErNElzezOc" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "wer_metric = load_metric(\"wer\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "e167261c6b0643c1a9de50f92c0590ef", "64222f57f03140b7b10a9927bb896098", "025f169226ca4f54a88b56e6446b0f27", "e25f1bbdf41b42e3838e81cc312e3098", "6dbaf83cde894cfb95979e911705dd72", "95fb631af45843f78daa5b9d0e3b59fa", "916ff79ba8d94fc38f439e51c70ed483", "311e38ea53854907ab63637bad7915c4", "bf519997271a46ab9832502c8376a439", "4d4f442c01694355b5e3660bd4cfd86e", "cb72ee2808bf4ab39f53a3714a09ddf8" ] }, "id": "tHAXlHO1ez7Z", "outputId": "49b65f90-7ef0-458c-f343-87624c3a8f1b" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e167261c6b0643c1a9de50f92c0590ef", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Downloading: 0%| | 0.00/1.91k [00:00\n", " \n", " \n", " [6100/6100 7:43:11, Epoch 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StepTraining LossValidation LossWer
4002.9599002.8649911.000000
8002.7357002.7377490.995095
12001.3012000.6685550.711050
16001.0454000.5686420.613676
20000.9069000.5576090.581506
24000.8200000.5502260.559146
28000.7413000.5969900.558569
32000.6872000.5816530.542845
36000.6340000.5636440.531448
40000.6022000.5780250.522937
44000.5705000.6036290.532314
48000.5408000.6118670.533612
52000.5225000.6105440.526976
56000.5265000.6034070.523081
60000.5154000.6094430.523370

" ], "text/plain": [ "" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "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 = 892\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-400\n", "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-400/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-400/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-400/preprocessor_config.json\n", "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/preprocessor_config.json\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 = 892\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-800\n", "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-800/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-800/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-800/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-ha-cv8/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 = 892\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-1200\n", "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-1200/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-1200/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-1200/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-ha-cv8/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 = 892\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-1600\n", "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-1600/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-1600/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-1600/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-ha-cv8/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 = 892\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-2000\n", "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-2000/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-2000/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-2000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-ha-cv8/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 = 892\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-2400\n", "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-2400/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-2400/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-2400/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-ha-cv8/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 = 892\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-2800\n", "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-2800/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-2800/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-2800/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-ha-cv8/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 = 892\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-3200\n", "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-3200/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-3200/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-3200/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-ha-cv8/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 = 892\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-3600\n", "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-3600/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-3600/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-3600/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-ha-cv8/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 = 892\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-4000\n", "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-4000/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-4000/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-4000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-ha-cv8/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 = 892\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-4400\n", "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-4400/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-4400/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-4400/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-4000] 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 = 892\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-4800\n", "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-4800/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-4800/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-4800/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-4400] 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 = 892\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-5200\n", "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-5200/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-5200/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-5200/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-4800] 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 = 892\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-5600\n", "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-5600/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-5600/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-5600/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-5200] 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 = 892\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-6000\n", "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-6000/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-6000/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-6000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-ha-cv8/checkpoint-5600] 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" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=6100, training_loss=1.261003189086914, metrics={'train_runtime': 27800.0598, 'train_samples_per_second': 6.982, 'train_steps_per_second': 0.219, 'total_flos': 2.5645201559735407e+19, 'train_loss': 1.261003189086914, 'epoch': 100.0})" ] }, "metadata": {}, "execution_count": 34 } ] }, { "cell_type": "code", "source": [ "trainer.push_to_hub()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 370, "referenced_widgets": [ "a7b6850a923647c3a1f3e63354b52dfc", "475ac5f196c24148815e5a4e87d7c377", "17042312fcab4786b948e781ad4d7ead", "da397ad8e4a94ef597364090c4f8e6d0", "356144ad9e524caf9fd1f0fa7dc80827", "7b809d15d24b4f89aeff544a9e95f223", "eed3462fb60f4c939b6e67a43da652fb", "0eeb65b2bb1b4447a3116a23714051e8", "1b405c99a9a24901aad625aa7912c22d", "5d4a88ff15be4c2988009c03e976d44d", "9adcf56c0f094295a0b82bffc8fcdc84", "ca2bca9353b246a59b171d33861f8f43", "d16c9b158aed4f28bb2a6b55702fd277", "69fc532a8d024926a2fb294230872e38", "17de61efffbb4da4b9972000dd434cbb", "9186b22406d3404daeb55b059151ccad", "4bba5e3ac21d48299d74ab3216103669", "a57138b30a664ecd97784fe65206c387", "4d3886eb267b48b5b2d888fd19e36e80", "7e1217bc769e47908183e3ad2dc986e9", "9d6447b1a5ba435681b6d0b5152a2324", "6e1f7790f7254218a1eebbb8341bce06" ] }, "id": "1DzGjKHifPGK", "outputId": "71a60ef0-ad3a-43dd-902d-e515a5539edf" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Saving model checkpoint to wav2vec2-large-xls-r-300m-ha-cv8\n", "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-ha-cv8/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-ha-cv8/preprocessor_config.json\n", "Several commits (2) will be pushed upstream.\n", "The progress bars may be unreliable.\n" ] }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a7b6850a923647c3a1f3e63354b52dfc", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Upload file pytorch_model.bin: 0%| | 3.37k/1.18G [00:00 main\n", "\n", "Dropping the following result as it does not have all the necessary fields:\n", "{'dataset': {'name': 'common_voice', 'type': 'common_voice', 'args': 'ha'}}\n", "To https://huggingface.co/anuragshas/wav2vec2-large-xls-r-300m-ha-cv8\n", " b10ee1b..19dfbde main -> main\n", "\n" ] }, { "output_type": "execute_result", "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'https://huggingface.co/anuragshas/wav2vec2-large-xls-r-300m-ha-cv8/commit/b10ee1b13924b4dcb001e932e35520c79c438059'" ] }, "metadata": {}, "execution_count": 35 } ] }, { "cell_type": "code", "source": [ "" ], "metadata": { "id": "93NnxhS_1EIV" }, "execution_count": null, "outputs": [] } ] }