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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": "0113dea3-3ff1-4cce-cb3e-062a7691fa00" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Thu Jan 27 18:50:08 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 39C P0 26W / 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": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "k6Ao2XfngKmI", "outputId": "42f3f790-8c04-4a1a-d7e8-26ba2f0d3626" }, "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 1s (2,440 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": { "colab": { "base_uri": "https://localhost:8080/", "height": 359, "referenced_widgets": [ "ca3d8b2c83fd4dc992e866c692750b0a", "d36eb844e8a64732bdd5ff333b6b80eb", "8ac172b545d342df8df31641a9631009", "1eb6b47df3914bbca1ae8cfc68328e28", "71716dcea2134b10a76a27134e757679", "a6f33f89c14f4f248dc291714cc98e1e", "6cfa0557129445578add0e6589ad8f3c", "36b2a248a6664d1daea9da8d9e0a7202", "6aab1244eb3b448995c298b272821bef", "a60309d7038f4a3799b76a75de5cf7f2", "dbbc623cff274941a3c8fe8996ed3a0d", "c662c63c77c94d05b62c1a0eb0e75b67", "1a78b5324d3541caba1d875e4f5b1f92", "0dfa212090a14912a217f338eadd5b45", "00726c6cb99c40a7a6b8d5eb3444effc", "b7f4f0b5788a4b0696f0af2dc04d6c37", "acd5f5ba75d843e49235bebfd179574e" ] }, "id": "1CCVxM2-hLbE", "outputId": "f8461c13-8fac-47af-b78d-122aed0c9429" }, "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, concatenate_datasets" ], "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\", \"mr\", split=\"train+validation\", use_auth_token=True)\n", "common_voice_test = load_dataset(\"mozilla-foundation/common_voice_8_0\", \"mr\", split=\"test\", use_auth_token=True)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 357, "referenced_widgets": [ "34e092a60d234970b9acc41bf01d12a5", "59ba762661254c1b923b0d44a67fda2a", 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"56621d103a2847d28d75250f8491143f", "875a31231a2e443ca1a6276ce1af6acb", "f80900fd499d427fbd414af8a8434682", "293c7bf725624b4aa6df63489050cbb9", "a87b664358494d66a76838719e76ab4d", "4bc0970a90464435abbf3ba2d4e69c2f", "1474d82933b1494ca514666d59b762e8", "2987f544f6e84000b3392ce2a84884c3", "c81ef9ca53324e5bb1cd9046fe54b122", "4be58d6efd3a4ef98ad844a2e1b4d9ab", "584c7356fd5743c18f82081db60b1b90", "a9db641d016940b5b85b2289f84d567d", "abfe7a5460334884b039ad885fca0068", "06508079146544f791dbaacb42a0de8e", "d26f4b09f3bf4b129a063bcf81461835", "97b6463da12a4b58ae7ecdfc269fc272", "f7a85a5476524ab48590a3a4db0c55f4", "1cba7cb3ee5e4dbfa0acb0b81d5b4bee", "1f0fe1c191164757b193e312ea46025a", "593e8b78943b4ad4b550895e3431087a", "af2ec4f16a9e4c08ba3d2de7684394c5", "e30eafba0d8243fb9d0522a4d4f24552", "e277de72873640d09a18fc19bf7e1c23", "7ce41e0137fe465bae1d814ada648f93", "a044f11741c440b48f88d96993d1d078", "c3b06379632640d6a82af1db0d3d0649", "b3e017443413460597aa500225c235c0", "4b182cfed85f42229c87f6dc0406ba17", "868ab296b4544570b448a409b91b336f", "ad50a0fc77f84ceea1843e3250193e00", "057497ff40ec4b1a99d00b6a70104c85", "5cc810ec30c74403815620659b5f58ac", "0de6db612e474edb96e6c55e84736748", "2885064ddf344b4d9878b627833689c2", "da7e27e5f31940fba03ba7dffdf5a694", "f3c5717788a14107968c04a47c7be210", "7480ad5cc1df4f0d972845cb0890beaa", "cfcf360b085a4cf09ff4ec69de534ef7", "5ef76aeee67741d98f5db643e3aff2d1", "dc6b03b392f047e68c854343f10afbed", "72e338c3bcfa4bfab50cafaa59271a4e", "cac2e43e8e2940f8a3b7ee38d63df7a9", "5cf0fedf07e849b7ad157980b6e21998", "e45826f02f0f469a8a93e1c662a6add9", "87a15fb8a8aa4caea485b8823603f6f7", "991f38ec9e2a4e0f8c78b9a19516cbb4", "c0fd25f91aa4483393f84c011ab7ea23", "71f35d92067342ef862b8c9e58c29e31", "ca341b4700fb42e6b627f3ed37fca841", "eb7e18d1178040f98cf81c719581b06d", "36618eb19dfb49a4805703f265edc6c4", "577dab79fca14d928e56a54e9f5aeb79", "3293c18b24de4241872310f76ffef917", "9e400d583448486eb4b0423bfab1e393", "4d3f19785de643c49b4284b3876c30e6", "3b6b24050fec4c4aa0cabe8cff618d34", "7d418e1965d7493a924db0b02effdbee", "c7ebe17517ec4bcab877973dafefe964", "fe308c8b89594b7fb2cd982de17a2160", "77ab9b4cdfb8474c98bbc2dc24d24f83", "1600f36034744208a60d1d5fce2d1de5", "5a21f386bf284975804dbf47afb6cb7f", "8bae609f57334f02b6eda253a47a5a9e", "60ea929c99fd4637b5348b6027437cd9", "77897aaabff64f4db93cb5fb1efcbc16", "6d38da9273e44400ad0511705231df4c", "4e825d5104544f47a6a4e288762fb962", "a3e593e72eae4293978b08fa6c69c36b", "700687294ccc4d7ab8638aa2de34cf12", "f51a891b557b42dd87c8c7db6bf623c7" ] }, "id": "hnwqYEFnhji5", "outputId": "1257b32d-49c0-426b-8889-99b90e63ff8e" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "34e092a60d234970b9acc41bf01d12a5", "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-mr/commit/579700a90edf82cbacef673e7e305ea8386f7bf8'" ] }, "metadata": {}, "execution_count": 38 } ] }, { "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": [ "2029fca60e2846de9e7fdcab44747a84", "27fea2e3fa964b6db7a54674ad45cb54", "04fe4ba7d327481aa859d25f6e35b23e", "509383be38a0437485e7ac3675088720", "0e49a6c61c604ced8a40d9533f7f8462", "f9eeb32c56cc4d1db4aa3cfee6f65050", "bcb673ae4fd442a2ae50e8f0bfc9dfe0", "3c2add9af4bc43d9bd003a1050a544dd", "95027ab4641b4d2cbfb9521c56022017", "048ea4e304eb4801b6fc230ebeeccf0e", "fcd59057e9244a5d97795cf01b20f3db", "f5ea16e6cbdc4c6187c64207649a489b", "044b977428dc42adb15411520114c3a9", "16b55bee016441c89636282e8a4de86a", "15ddd49101a646409e3e0a378fe495dc", "cb15bc09e2c14a7089d35cdc5792ab9e", "0690faf96d424cceb5b48ce6699617b3", "45199db5d1e848f89d8b991f204688ce", "376cf5f01bbf4cbc925f60f2564e825f", "f897f4b0d3934e9aa668a9c0484ed8ee", "27e51a2569dd4405bab3eae71a3aa330", "9417e7f296ae4cc5b18e41ccc9d6dd6f" ] }, "id": "Uxt4G7XweZxf", "outputId": "5b1d33cc-93dc-4334-8748-612c2b23253b" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2029fca60e2846de9e7fdcab44747a84", "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": "f5ea16e6cbdc4c6187c64207649a489b", "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": [ "752699eea4944dfc87d383b8709fb810", "0f8fe3ebdac74e299694511ef3c3582e", "58f899a7b6c74db0b2268a442c18be35", "84f7e373503743a8ac33cc70d1ffeb76", "ee945c3b01c0490a9ccfee68eaaa3283", "2e769a575dff43e69fdbdc10debdedbb", "160248765b014defaaa4bfbce207a8ee", "127537cdb5214476ba1f04ca6273265b", "5dfad1bf272d4914a1e4c99b32f799ff", "5e1e2943cd5f482fb055085b21339a63", "372186370fe94f4caa0fed0585aa52ed" ] }, "id": "tHAXlHO1ez7Z", "outputId": "b264fe96-9b0d-4c12-a439-059f7a6236a1" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "752699eea4944dfc87d383b8709fb810", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Downloading: 0%| | 0.00/1.91k [00:00\n", " \n", " \n", " [4400/4400 6:34:02, Epoch 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StepTraining LossValidation LossWer
4003.7378003.5047361.000000
8003.1707002.6165840.991175
12001.4942000.5777740.692740
16001.2058000.5168430.636181
20001.0558000.5105080.606899
24000.9488000.5150870.608905
28000.8588000.5156610.598877
32000.7991000.5179090.574007
36000.7545000.5348220.574007
40000.7144000.5517710.572403
44000.7041000.5479110.574007

" ], "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 = 306\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-mr/checkpoint-400\n", "Configuration saved in wav2vec2-large-xls-r-300m-mr/checkpoint-400/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-mr/checkpoint-400/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-mr/checkpoint-400/preprocessor_config.json\n", "Configuration saved in wav2vec2-large-xls-r-300m-mr/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 = 306\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-mr/checkpoint-800\n", "Configuration saved in wav2vec2-large-xls-r-300m-mr/checkpoint-800/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-mr/checkpoint-800/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-mr/checkpoint-800/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-mr/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 = 306\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-mr/checkpoint-1200\n", "Configuration saved in wav2vec2-large-xls-r-300m-mr/checkpoint-1200/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-mr/checkpoint-1200/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-mr/checkpoint-1200/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-mr/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 = 306\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-mr/checkpoint-1600\n", "Configuration saved in wav2vec2-large-xls-r-300m-mr/checkpoint-1600/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-mr/checkpoint-1600/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-mr/checkpoint-1600/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-mr/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 = 306\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-mr/checkpoint-2000\n", "Configuration saved in wav2vec2-large-xls-r-300m-mr/checkpoint-2000/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-mr/checkpoint-2000/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-mr/checkpoint-2000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-mr/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 = 306\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-mr/checkpoint-2400\n", "Configuration saved in wav2vec2-large-xls-r-300m-mr/checkpoint-2400/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-mr/checkpoint-2400/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-mr/checkpoint-2400/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-mr/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 = 306\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-mr/checkpoint-2800\n", "Configuration saved in wav2vec2-large-xls-r-300m-mr/checkpoint-2800/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-mr/checkpoint-2800/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-mr/checkpoint-2800/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-mr/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 = 306\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-mr/checkpoint-3200\n", "Configuration saved in wav2vec2-large-xls-r-300m-mr/checkpoint-3200/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-mr/checkpoint-3200/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-mr/checkpoint-3200/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-mr/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 = 306\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-mr/checkpoint-3600\n", "Configuration saved in wav2vec2-large-xls-r-300m-mr/checkpoint-3600/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-mr/checkpoint-3600/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-mr/checkpoint-3600/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-mr/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 = 306\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-mr/checkpoint-4000\n", "Configuration saved in wav2vec2-large-xls-r-300m-mr/checkpoint-4000/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-mr/checkpoint-4000/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-mr/checkpoint-4000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-mr/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 = 306\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-mr/checkpoint-4400\n", "Configuration saved in wav2vec2-large-xls-r-300m-mr/checkpoint-4400/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-mr/checkpoint-4400/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-mr/checkpoint-4400/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-mr/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" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=4400, training_loss=1.8104798316955566, metrics={'train_runtime': 23651.2936, 'train_samples_per_second': 5.902, 'train_steps_per_second': 0.186, 'total_flos': 2.4058662657177895e+19, 'train_loss': 1.8104798316955566, 'epoch': 200.0})" ] }, "metadata": {}, "execution_count": 53 } ] }, { "cell_type": "code", "source": [ "trainer.push_to_hub()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 342, "referenced_widgets": [ "900220d29d30456d9530a74324ca3eb8", "f40103b9821e4a8cbb579c377ac25888", "8d899f66bb5547ab9b8eea71f2eeca08", "4bb4a65c966942b681e3d14a7ab56b7b", "b2c6aed4cef44009a0a25682ed218718", "80e65ccd4c894c89a2f512ee641cb17e", "9cb3f5aec9884ce89b67f95b6e9ecbd3", "24be5091747d4b0cae6aa272b811cd78", "2cc9b2c77ebe4483803c626251121228", "a82fb5fbed8649b6babfa9ea9f004fc4", "ece3c0cf182f4a3e8e1d7f6e888eb97e", "91d0652774f2409b820fe2ac8c05ca86", "9b011071ae19400bb07007a84f7de2e6", "105f5b34bf3b4aab84346b17dafc3c95", "c46100ef53994af3bc31f24a63a93649", "37920898d5ab46118fb88bb3a0c96229", "b3ee6ac43f2e4667a235d0c484c67082", "a595f35297de4af19a3cd5a1c0c37196", "4fcd686099e54d28afcceb5ef93b56e3", "3a2adcc4c7134a65851cea7914b8b46d", "1aeda2afefb144e5989380d6cc0a748e", "20d79a9262bb443782fe37a549c24469" ] }, "id": "1DzGjKHifPGK", "outputId": "f184ae60-cfd8-46b2-90f9-b41ca9c8da61" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Saving model checkpoint to wav2vec2-large-xls-r-300m-mr\n", "Configuration saved in wav2vec2-large-xls-r-300m-mr/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-mr/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-mr/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": "900220d29d30456d9530a74324ca3eb8", "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': 'mr'}}\n", "To https://huggingface.co/anuragshas/wav2vec2-large-xls-r-300m-mr\n", " 60f764e..fb33813 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-mr/commit/60f764e9872c42651f3f4144c8b0993a862d4d3b'" ] }, "metadata": {}, "execution_count": 54 } ] }, { "cell_type": "code", "source": [ "" ], "metadata": { "id": "93NnxhS_1EIV" }, "execution_count": null, "outputs": [] } ] }