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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 } }, "cccb9f89f25043f2a305120ce4000eac": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_view_name": "StyleView", "_model_name": "DescriptionStyleModel", "description_width": "", "_view_module": "@jupyter-widgets/base", "_model_module_version": "1.5.0", "_view_count": null, "_view_module_version": "1.2.0", "_model_module": "@jupyter-widgets/controls" } }, "474a5f28f8a6410a8febf0ee0da60716": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_view_name": "LayoutView", "grid_template_rows": null, "right": null, "justify_content": null, "_view_module": "@jupyter-widgets/base", "overflow": null, "_model_module_version": "1.2.0", "_view_count": 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 } } } }, "accelerator": "GPU" }, "cells": [ { "cell_type": "code", "source": [ "!nvidia-smi" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "M2b-7B0PjEvG", "outputId": "c77d3c33-a63c-43e5-fde2-101af13cdac6" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Fri Jan 21 05:56:32 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 35C 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": "25e24d57-5818-4850-85dd-a6b9c91bcd1e" }, "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 (1,669 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": 415, "referenced_widgets": [ "21aca65c133040c2856f187fe640763b", "6b943f96e09b41468710919f47af8e4b", "70305cdcc0df40fe859f0a58dcb1d5e7", "93e74b19f00a46e2a57e818858a324b0", "f92042aafc9c46eb8a873fdacddec5dd", "b8a7119fe079466698f8f36d0def7adb", "9f2a4d6126b44098b8d32245650d42d4", "c71f0a59cad245afa24064974596fad1", "a04185b2fe324aee8cc5daf5c71ebf7a", "ca54e3e9d8ff4d22982c2f3b65718328", "f9155fdfcdaa4f8794af0f21643ee5e1", "c31a4614514448a48eb3d8af9b4a2bb3", "bf1c397ab5934920a73d22965309c3e5", "937b29e1b1044c2e836db961affe18ef", "cd79f8fda0074f07bb65c3f9c5ec5910", "642d087b63aa45c5a66c147bef16b23c", "5bf3b34592464d5da47643c85382f160" ] }, "id": "1CCVxM2-hLbE", "outputId": "cf91d49d-ec85-414a-a616-cbf3707c74d2" }, "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": [ "common_voice_train = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"as\", split=\"train+validation\", use_auth_token=True)\n", "common_voice_test = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"as\", split=\"test\", use_auth_token=True)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 377, "referenced_widgets": [ "7c2d0d747a7f45b4bfb9faf9dabfed22", "4388f53476864eaa844de45078df97f5", "6abaaa2e7d4944288a197692c6f231b4", "33004f85239342fd8c939cd4bc05214a", "de2d8f1393ac46288eff5db847b4ceeb", "42ec22107eb8476893e60c85b9d0b38c", "f4b3cfa9ca324f80a4fe7613bc114389", "207f62c3dd8240f995eca2c7738ba4b3", "2433a932d4c54bb28d50a5893064dd34", "63b92ea2416f4bea8e5101387da2f50b", "2ddd3326b62b41398b00def0f9212d92", "03e2ae13d5ad49f58e8b3d6aca0155f7", "25d5782843d041ba92684fe6308ffee9", "5d34059333434b23aaeba11c3456d278", "9a6ed602bc174a45bf991fc1aea6690f", "dd9fcb9a246643ddb6c453a9e4ace30e", "aa1f683863f7426284fd79caed1e1ec2", "2bd2ca29f6e749bfb575143c215f5a53", "73477c7cec8f4ff083f6edaf0a19e8c1", "262667d90d8145d5afe211d796a63f91", "24237d2783c14a34b51933c9ba0eb561", "3a35b26b9d6d493aae78609d8cf4a364", "67578b8e12c14c6c91ad27fa456e5b67", "ec901a9efcbc4073880037e3d430390d", "3c895ab83b8b40fba010637726e31cc7", "52435a3759bc4d338df64be42557012a", "49bc964983494836ac9bc0ced81adb4f", "7d3a136478f9418089bcf6a871d9b51a", "caa17aa9b71a4df28ab256fd0764a0d0", "b41001983c464e88b36bc3f8eab0ab81", "0a422d338c4e42c5b02a06a9d0d225a8", "89291f0e4ecb45d0b43c9e3eec47f06c", "d09e9a036b76461f895d0b8e50db0ead", "ef011176117746e6872beff0abbdfdeb", "2ae449cd70844ed7b557d75dd2e9e395", "8f8f28e99e934fe7af00a4ceaa27c796", "a0aa59ab785f4cafb28169fbe275fd50", "7d0272df40d54c999c79686c8b479382", "6f0fbae720d94f8b826392b8c37875c9", "f3a4c1a80586427cb1f2f39127065c7e", "7aaa426d49dc4896b421de1146762dfb", "bbf963e092d44ed49714c6dae0d18991", "15844aceecad44d7a4c1f2e566b8cf85", "c300bed6990b49b6a69ac23011074377", "cb962d0e454f4265931848486266986b", "18e5af538be741d786ac89130a631991", "edf0801d0a814d3bbdabb62c6e7631bd", "0a094d5cf5cd401c840b3326dcff776f", "f905b16926724c579971c0a0f43f8ff6", "cc431579fa894ad583a18f337eeec6db", "b3d965bf9f2e47d2a9910ae9cf637fa3", "521307fb7b684031a7b1fc1b94dd5c51", "5da5b1ae490a43bcaee81ebf01488909", "dbbb2cc0b71a4861927f7a67f3b4bd39", "2387b20271ed4c8983decadf43384175", "90df4dd410dd4e379e6140a2f0706457", "d1a8c9c474014dcabff33fae458af18c", "634ac083a729450b8e9a3381b67f2c16", "a0d7cbdec9394e278096e2efb3bae667", "6dbf21baf31540c685b85b2f7344bbf4", "aabdd785eaa542b2811e40a42b53a206", "2cc40491fc37423f9e4594d3aa8465b5", "4176bb0afcb44cbdbfa2740b977630bf", "cfa434561efb4c4cb25cb774b97e598e", "e8ea887a702f4fa69b055ea403fc84cf", "d0d11930b62e4bc389631d855f31c1ec", "17742327cdaf4f15b5ca5987613175e6", "30468bf566674c3a82f453b467f2ad18", "a6cccc4691ec4a5c9d37fe2d78ad074c", "2b357dfc70c543908a007be1b9f661cc", "c53b5722c41d4d7cbd1d6d569025801a", "a5c8464497ab4635bb61b6a231678757", "1af65bbf4a614efa856addb4e5f63d3b", "de916057b5d84854963a5b71d015c687", "a07853e466994b6f96cad2053f7cc5d7", "341db2e46632474c851f6786b79730c6", "5fa21c56409c4514b43b8ea63a5d94cb", "af9d96aaf1fc4d339a7b59364d74f746", "f2e7075e9e6c4b3fbae24fb2316a70c6", "23af1b375c0e421da3a2226795eaf433", "eddbbface586430aaa9c09abf4c1e2cc", "ebab006dad1d4f97bda7550f9a9def86", "8f501559fd994e65bb93a588d34f80dc", "c336189d896e419895d3c4666b11eeef", "be4f4d10cf174e8697e493aa57eea6cd", "5c9af6cd81c0419e9505b9c8fcdf63d2", "5126b04be5354dadbb73dded813715a6", "292bd6caed0648259b3d348256a91c18", "676fedef8e1d4497b4419749ffeaffb8", "d92413b401c74d02bd7563d418141154", "de559265846f4df09f556e5604b4140d", "71ffcc77e2d2488797dce6ccd5dd6e38", "adb684b0c81a440bbc33bb37ac4ad455", "df3b120419124fa29219974af5779a2a", "9ea0758b6de4496ca24d625a11ebd245", "14fe58435c454d7f9c853dca9b0adaed", "eaeced56cfa041bd85dfd8f8f96d0f4f", "7a02ae432ca9408291514a389ea9cbcd", "f4f0705f12cb472288857634e2ca411e" ] }, "id": "hnwqYEFnhji5", "outputId": "37ab3614-1be2-4687-d19b-2bbd4661a7cd" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7c2d0d747a7f45b4bfb9faf9dabfed22", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Downloading: 0%| | 0.00/9.79k [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-as/commit/46fe0a4ed7ef2244e91dfee3a9153f0ce7c7da47'" ] }, "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": [ "b7d43dfcf210417187184f0d42b34167", "51f4c100f25e4981ad248b61b45bede2", "b6132ef7565e45088a8c460e0d45e963", "07d39e51cd88427eb95d95b12b9aa8a1", "ecf4f5d08bd34878a73a9d860215ce80", "48650f0ffffa48e99e7daed7c94a74f7", "517e4cd235064167bb6ac0fb8c6cdc9b", "d1da30c22c35442b935e05f4956dc24e", "bae5f175cfad4939aaec8c80d2a0ac25", "05adce0052d84212bc3131c4ed7e0bfd", "5e383d63ef1b4b7cb91a2956687e17d2", "aa593380281b405eab58917dad8ede17", "8cad75d42e1246ada295d6fab320620a", "5fa59687b8ef46cf80d81a24b42dbbe2", "e67e54e01c1a48ddbdbd2c150d66ce35", "ebdd55db394e48aa8de21d1577649993", "f88950b5facf44fdb2b3d7e82eb5d13a", "ae60e6cd26d441248414fcfab83a9868", "8c481f3cc9b6453c898925286dc3f78c", "86e8c7008cea4631b6b488ee86673571", "ffbe4546179a41cfab27860b51d12c30", "abdb7d1cba714b90b68640316bb64d0b" ] }, "id": "Uxt4G7XweZxf", "outputId": "dc00290c-26ce-4d2d-f45a-2b8ce96b64d3" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b7d43dfcf210417187184f0d42b34167", "version_minor": 0, "version_major": 2 }, "text/plain": [ " 0%| | 0/624 [00:00 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": [ "4e57d172ec1744dd92ec9db65e19cd8b", "de60c992211448dfa5e2d1d793cf020e", "7ce662a81a164e168d62baad50fb6dea", "48eafe794e374930b259c304fc70277e", "6ae8afa755244a4ba7e0dba849813dfb", "520354763bd2412dabf440cf37cff4fd", "caf93c6dbcc14eafa8572a1a132b1f59", "5255d8f8d60348de8aba3fc5a3bac1a4", "73f643efc2034dd1982ee0f3265c03a5", "ce34e2c2c25840d782d35ba411dbfa40", "4d1d3d8dbaa14d99834be53d96f50dfa" ] }, "id": "tHAXlHO1ez7Z", "outputId": "c213f39c-fad8-4bfe-f062-136f0956cdf3" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { 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StepTraining LossValidation LossWer
4005.7027003.4156651.000000
8001.1638001.3497870.746089
12000.2266001.6147380.727313
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" ], "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 = 281\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-as/checkpoint-400\n", "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-400/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-as/checkpoint-400/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-400/preprocessor_config.json\n", "Configuration saved in wav2vec2-large-xls-r-300m-as/preprocessor_config.json\n", "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:1093: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", " return (input_length - kernel_size) // stride + 1\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 = 281\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-as/checkpoint-800\n", "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-800/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-as/checkpoint-800/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-800/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-as/checkpoint-400] due to args.save_total_limit\n", "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:1093: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", " return (input_length - kernel_size) // stride + 1\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 = 281\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-as/checkpoint-1200\n", "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-1200/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-as/checkpoint-1200/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-1200/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-as/checkpoint-800] due to args.save_total_limit\n", "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:1093: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", " return (input_length - kernel_size) // stride + 1\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 = 281\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-as/checkpoint-1600\n", "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-1600/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-as/checkpoint-1600/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-1600/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-as/checkpoint-1200] due to args.save_total_limit\n", "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:1093: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", " return (input_length - kernel_size) // stride + 1\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 = 281\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-as/checkpoint-2000\n", "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-2000/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-as/checkpoint-2000/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-2000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-as/checkpoint-1600] due to args.save_total_limit\n", "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:1093: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", " return (input_length - kernel_size) // stride + 1\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 = 281\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-as/checkpoint-2400\n", "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-2400/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-as/checkpoint-2400/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-2400/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-as/checkpoint-2000] due to args.save_total_limit\n", "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:1093: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", " return (input_length - kernel_size) // stride + 1\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 = 281\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-as/checkpoint-2800\n", "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-2800/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-as/checkpoint-2800/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-2800/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-as/checkpoint-2400] due to args.save_total_limit\n", "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:1093: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", " return (input_length - kernel_size) // stride + 1\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 = 281\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-as/checkpoint-3200\n", "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-3200/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-as/checkpoint-3200/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-3200/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-as/checkpoint-2800] due to args.save_total_limit\n", "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:1093: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", " return (input_length - kernel_size) // stride + 1\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 = 281\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-as/checkpoint-3600\n", "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-3600/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-as/checkpoint-3600/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-3600/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-as/checkpoint-3200] due to args.save_total_limit\n", "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:1093: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", " return (input_length - kernel_size) // stride + 1\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 = 281\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-as/checkpoint-4000\n", "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-4000/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-as/checkpoint-4000/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-4000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-as/checkpoint-3600] due to args.save_total_limit\n", "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:1093: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", " return (input_length - kernel_size) // stride + 1\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 = 281\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-as/checkpoint-4400\n", "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-4400/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-as/checkpoint-4400/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-as/checkpoint-4400/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-as/checkpoint-4000] due to args.save_total_limit\n", "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:1093: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", " return (input_length - kernel_size) // stride + 1\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=4560, training_loss=0.668572286451072, metrics={'train_runtime': 21486.0103, 'train_samples_per_second': 6.97, 'train_steps_per_second': 0.212, 'total_flos': 2.2650880010566533e+19, 'train_loss': 0.668572286451072, 'epoch': 239.97})" ] }, "metadata": {}, "execution_count": 34 } ] }, { "cell_type": "code", "source": [ "trainer.push_to_hub()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 370, "referenced_widgets": [ "7bb658e851f14ba8b34c287a03ce2aad", "734596d5acbc44e0a3bbd1cf92d7b7ee", "c854c2e9aab044d5b2018742ed9e591c", "723e635a06d346cb857bc7e04a015dca", "510e24835e794bcb8876f482ed43d281", "686d25fccca94e04b7beaff7686c5e0b", "013198b21fdd46c5b427b012e5480f12", "2249f8dafe31446688f6b486c8c3cdc0", "efd77c3dec8548ea9d6a47a2ced6016e", "49ba3fb70e3d40cbbb3349c1202298e5", "c3fff53a90d44236b9b5ddcc44d1ca2c", "0ff572a0c3a0463dadc46ee97e1ac546", "4015034b0fe84516a900073b6e612096", "32f1d153753a49f8b5c1003910c998ce", "32a812df48524cf1b1724513f2445aa1", "2f80a8b66c384572a1b08b8bd81cef85", "94352f05dbdd44298046f92b6edadede", "3fe3f8a724f9480596d7c5c0d05c008d", "bc6378868a194e6989548b3d4f8decbf", "321c6979d8b54a3aab07e2b7b40779ae", "cccb9f89f25043f2a305120ce4000eac", "474a5f28f8a6410a8febf0ee0da60716" ] }, "id": "1DzGjKHifPGK", "outputId": "6a7809a4-b5ce-4116-a557-f99332e2af28" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Saving model checkpoint to wav2vec2-large-xls-r-300m-as\n", "Configuration saved in wav2vec2-large-xls-r-300m-as/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-as/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-as/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": "7bb658e851f14ba8b34c287a03ce2aad", "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': 'as'}}\n", "To https://huggingface.co./anuragshas/wav2vec2-large-xls-r-300m-as\n", " 87eccaa..d297d9f 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-as/commit/87eccaaad11b7893d86f1dfcf0cd64ef7483b868'" ] }, "metadata": {}, "execution_count": 35 } ] }, { "cell_type": "code", "source": [ "" ], "metadata": { "id": "93NnxhS_1EIV" }, "execution_count": null, "outputs": [] } ] }