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"_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" } }, "01467d40c1a640ad810c6022d63d1fec": { "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 } } } } }, "cells": [ { "cell_type": "code", "source": [ "!nvidia-smi" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ktnAD1ruEEKY", "outputId": "9660457e-20fc-43c3-d907-3d79099740ca" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mon Jan 24 03:58:42 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 T4 Off | 00000000:00:04.0 Off | 0 |\n", "| N/A 56C P8 10W / 70W | 0MiB / 15109MiB | 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", "source": [ "%%capture\n", "!pip install -q datasets transformers\n", "!pip install huggingface_hub\n", "!pip install torchaudio\n", "!pip install librosa\n", "!pip install jiwer" ], "metadata": { "id": "DtlJ3Ey1Bafu" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "!apt install git-lfs" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "OFHZExbKD82m", "outputId": "83a32fd0-363e-40e4-9ad6-f4b3113fe476" }, "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 (2,377 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": [ "7278058e7f91448180f7e817413dbb31", "fc59fca7658c49afa2743fd85aa6ef28", "3d8f3821d8b54c139e6bd0be1a9070c5", "bc8ad7d63d1f4d3e8bda03bde2b7fd54", "69587f67b44049adb8d1ddd458307c03", "584a19be9ee940bc95dceea591370bf5", "a267aba23e974c2d8eef46570674813e", "3994eaad1f6249dfb14ac91510ec8fff", "2e08758575df4eb7a4e587352d4f6de3", "f92db9f6f4384721975b309cd38484c8", "ba5db819cfaf4879a077ae7d0a3b745b", "10c83c8849884e0dbfb52e996b53eb03", "74236e8f309540a9b2dd1ae295ccda1e", "a03ed455454941fc92851c34e636e479", "43a486f5098c4f6da908ee74af58b65b", "c7debde6880b499babc85fb43a5fcce0", "33b6c66948b94924a8a4e9c9f3235fe8" ] }, "id": "GsGvBYj-aWkk", "outputId": "63292a34-7d53-4a26-bdec-58f6acf2317e" }, "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": "Zo8s-AlYDyCj" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "common_voice_train = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"pa-IN\", split=\"train+validation\", use_auth_token=True)\n", "common_voice_test = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"pa-IN\", split=\"test\", use_auth_token=True)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 197, "referenced_widgets": [ "4bdcddb6b60f4bb9bffc01ead12ab011", "706d1d4d5cbb4839a4667c230529dd38", "27b63b3365b449e0818664fc9735f498", "d764826101824bbd85e7ec2c896cc7d3", "ef730d72226a4dd7857d738b107b47c0", "9ff01020ed2545618d4d24ca998355a6", "07343e559bee4006a758b61087fb7c24", "afd9b1fc9ef44a7fb06d5a6105ccd3b5", "4cf1fb1e21c4405ca033e7e841f105c4", "79cef258ce2845ea9efc5c89d086df0e", "fa17e9749a414b42aee3a2202a66e16c", "7008109b38be4703ade3baf07218c2ac", "10663ea532ff45e190200ddcb903214d", "dc0462011a2a42ee88baa4c84f4714a1", "2dbbca93a268431284af4e420d8518ae", "c5bcc8b35c3343899e4aed1ffee38dd5", "5bdbc0482cf1472880f03c46efcc3fb5", "b6442bbc25e14c1b872120d2de0634e0", "fd60e7465e4f4e399cf5f576f46d1949", "f4c5f598706d4dc3a7f5672c89faa3a1", "b7d52425d4c34ba485a178abb6e50a57", "e0021b60ee5a42799beb39aa41fe1e16", "87a771d299d4434e84095f890220a154", "3a8ef8287ba345358d85696b84b29111", "b8fc2e72827b4175abe4d6541d5890d1", "c24e5d7794194e25acdbe9cf956f8eb5", "02790ca7dc38463fbcff596d98201012", "d1fd3d31fcb941a59c70ac90415c35ec", "008a2fa706124d67958b46dd50b6084c", "879dd431b61a4dc9a0c2745a3006749e", "44f83be3dccf4bbf8414ee34d4ad33f8", "41daf78bc24946948299f643b40e93ba", "f8bf0c7086494a1fbf6809a6c093d5cb", "e8860b5c46a5429d8fb7dbd2ac6b23bb", "dfc93a9c5f454f07b7304acdca017130", "a8b6b560a1ff486b8555dc0bcb33dbbb", "0d1edd1525f94a6d9091ef811081b6f9", "2fb1a8647005409a83615ec853945985", "bdd3f83895f0498aac5d4515b09a9739", "808093ddb0f2459b8df5eebb1f1b6146", "cd418b92299047bb96e173c5c66c4bb9", "a99c4ec0d6c84598971bde90fb92cf62", "3e66c2d16cfe458091a6ca2bffc2eebf", "71211e45c46c453fafb94b3c89e7526b", "1842990a86c4465c9a3e8bc35b8be6a0", "086f074fb90c4b96932306222b36204b", "11d1f0a02bdb438c8be7cf8b901d3201", "9bf65983247544068306cf4db54f806c", "17171ef27c674f3c9469cd2ab83c5ceb", "96c55f2b8f8c48bc97063527677008cc", "797e49bc19844628a8ece6183d29f2f5", "a7e37b3cb0754ea7a55530bf2d39bac3", "d09cb952f04048948e711921b8364a54", "764c0b5eb10c44edbed505f7a54dee3e", "24ebdb1ce24a4fbebf75ffd3fe596c2f", "f7efe021ab9b468885914b0af8f6e2ae", "7c20d9c014c74c81b43ed4decfa98c3f", "35c5c1b3c422433eb9c30c651d4bb815", "b74fd968aa684eae8fee3771e2cd703c", "f1222fd5bfdb4c9494eb7c4102f8970b", "adfe1f1172ae430394642a8b1e8fb069", "918f14420a2a4d06bf279f8676b41682", "3c6e6d908164425b957157887a5e099f", "264ecaa208c4423faa45cfd1104cc245", "2265050f0f20415fb816aaf7d57ef030", "89077926a18b46fbb1e6c093ec5fb21f", "7994d9cc75bf44c4b1beac50f5b547b5", "c0d790a6de0c4013a54c618f4da517aa", "429d6107d14b46b0b477ab0a63242b1f", "35baf7ae718840c1b1c9ffb84f4e0022", "07769a3070bf4f0391d7fb17a6d9aa7a", "435104d55bb34cdb87cdfb8531b6d113", "42477aa7303a44b081d19ddd37e90668", "e6f98248545f411aa24d6ee9aac5186e", "0e7bd9ae04414578af4d506f96125e45", "f788262587164c4b9c11af603cc08f75", "43a9ec473faf4185a197aae2725f09b6", "40313c4feb6a40ea9387c22bfef3042a", "a5c944a14e294382a3a0ee0aca3ebb53", "f769533a60ec47c8a9cb9cf80803b126", "ad73dd885cc244c0866ddb0db03fe2df", "3ea7cc9d16ae4295a6b89b9ef65f090b", "f4c96f2433cf4090b142c88c3cdda27b", "048d2d48ce4944d9a395052fbe05b607", "21915218c26f4e798409507a658cd2b1", "f3c9fd4ac3ca495181ef5668cd2a478d", "689ef7bdf0ec4de5a5a904ad0ca14ccd", "acf83ae3400449eeb9f1c0caacbc4121", "6eae98c905d345f5a3b2ea54bb3b64da", "d1ccb9ebc7d148fdbf1a5608beff3b05", "e4b5d01a63694ba380090151c5cca827", "8ca19a66de404c30a2b409ef87506b75", "52385d6b83c04b59b08b7ed3aa0c5247", "83e0a6bd38dc4fd1b1c998f0672b4420", "0710a805830f448f8bff9852dd46681e", "caa0c7c8dfe94fe1932130f4705742ca", "8d7aae4dd1554d75b190b1c4499c9fa7", "f75c881bb0d24a118b6fbeedc9b70a5d", "01467d40c1a640ad810c6022d63d1fec" ] }, "id": "0R2fwMfAD_oh", "outputId": "cebdfb7b-b097-4ca7-a8d9-b37223159e10" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4bdcddb6b60f4bb9bffc01ead12ab011", "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-pa-in/commit/50e2ea63d2998b4c2877a0b6cda18c59a518a357'" ] }, "metadata": {}, "execution_count": 26 } ] }, { "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": "pZMN1_9VFoR4" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from transformers import Wav2Vec2Processor\n", "\n", "processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)" ], "metadata": { "id": "OdqVLzfpFyak" }, "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": "__QcVbmkF0ZD" }, "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": "XH83kfcIF2HI" }, "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": [ "b50e08d9e0bf4f3c90bb608a3d6b1d50", "e15e139821a7485bbd1b521aa45991cd", "c2627eadb317476081ae3f9c35c5379d", "a81441f80b784ac1bdeaa628d0e5bbbf", "6820bdf0286a4bb18f029ce4783cd79e", "c8493e82762d4cb488fe46c0cf3c8e7b", "abdb870b78ee4eae9423c4cb7ca3638d", "ba6a4c6b276a40248aef7e258ededb15", "0677e69f85504bdfbb68e5e3e477721e", "2ff58f69811a49bbb3f9927de9d48083", "89cb89626f5f4fb0961dbab61bfa622f", "23de9c43f3bb4b51b8195dc6e952c5a6", "2f14f97876ee4df7928b1626083f407b", "0720fa020c7443ff8cb5e572fd9c0e95", "e0f219fe27ab499184b5090d20f7f8e9", "429005e4c820460da817d572bef545dc", "905cf51a59024fb2aef9d72fe983690d", "3322845a900a4cd292ef3c6cf2316ab8", "4d409ca3df0347279007dd8340e40bb0", "914e339753474eb785f2497e31c1598c", "50e6065d8d83463d80f8916d93eec66d", "a01e54f409e042df9cea61cfd658b565" ] }, "id": "K3eu7blnF4x1", "outputId": "b70737db-4fc3-487c-bec5-e359fa702673" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b50e08d9e0bf4f3c90bb608a3d6b1d50", "version_minor": 0, "version_major": 2 }, "text/plain": [ " 0%| | 0/781 [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": "bsKE5G8EF6nS" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)" ], "metadata": { "id": "pCAvRcS0F8qm" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "wer_metric = load_metric(\"wer\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "cca666050afa450a830b4180b86fbfa8", "d1440acd37e448c7947c9c5d02c268b7", "975d035a8d144c8a9f401e13f9a7e958", "3558a09afb2c4f09b4e53e301e08f59a", "9dbebe31e77c4b5a92232201f59fe78c", "9028021013a849d19c623d2db849b90d", "f9b3936dffe44c7994bd23c6b3f64886", "65aedab868c447808ad818ebcc657c1a", "1e0441a1239c44a4acf822a1312b76d3", "57bf63298d1d4ac99c2849e22c44e980", "ec8f56e692234e3c95d4bb842881f548" ] }, "id": "i3VkPgBhF-OK", "outputId": "ae458327-2e27-43f7-dced-30bfaba56174" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { 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StepTraining LossValidation LossWer
4006.4804001.8460611.000000
8000.4740001.1017650.662428
12000.1389001.1918190.610336
16000.0919001.1889410.605824
20000.0657001.2265770.593109
24000.0479001.2511610.590238
28000.0355001.2548080.567678

" ], "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 = 343\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-pa-in/checkpoint-400\n", "Configuration saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-400/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-400/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-400/preprocessor_config.json\n", "Configuration saved in wav2vec2-large-xls-r-300m-pa-in/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 = 343\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-pa-in/checkpoint-800\n", "Configuration saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-800/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-800/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-800/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-pa-in/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 = 343\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-pa-in/checkpoint-1200\n", "Configuration saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-1200/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-1200/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-1200/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-pa-in/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 = 343\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-pa-in/checkpoint-1600\n", "Configuration saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-1600/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-1600/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-1600/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-pa-in/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 = 343\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-pa-in/checkpoint-2000\n", "Configuration saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-2000/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-2000/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-2000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-pa-in/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 = 343\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-pa-in/checkpoint-2400\n", "Configuration saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-2400/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-2400/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-2400/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-pa-in/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 = 343\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-pa-in/checkpoint-2800\n", "Configuration saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-2800/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-2800/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-2800/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-pa-in/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", "\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=2880, training_loss=1.0195181330045064, metrics={'train_runtime': 11634.7947, 'train_samples_per_second': 8.055, 'train_steps_per_second': 0.248, 'total_flos': 1.4789924329646787e+19, 'train_loss': 1.0195181330045064, 'epoch': 119.98})" ] }, "metadata": {}, "execution_count": 41 } ] }, { "cell_type": "code", "source": [ "trainer.push_to_hub()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 370, "referenced_widgets": [ "115fbb1c21b145618fc0d391e201f922", "d30c33ce5b674dd583cb880197136fad", "e0810a85c656407d935c4959c29c52a5", "092a6ab3acac4c22ad6dc15777ab031e", "14b5af3cbe594ff282ae0415ae8e6d18", "a8bd35737fd4472b98494d6b048ae119", "997561dd68bb418ea86d6c957f919bfa", "e7ec33dd62df4063bb6d0abd3c8c1aa6", "d9cc3eaea19644f1baafabb4cf52f8f0", "93eb52140f654537be2b64c045a0d65b", "710fc56bc545449da082a953769a5a92", "81557dc72fbb4a18b2207e61acf83968", "fd36cbd237054c569438ecce05d20120", "9516bb75b27841629f9bb735a5240c5a", "a35b729be41f4b11bb8e4d94541476ba", "4112dded89304231b1438af48e7bb9ec", "1f5913397eb14f5c9226af6a864df7e5", "13ae6e7ece3049ecb241c3cc94fb279c", "25efb9576ecf4d3eaab3125b5d1c7c20", "7eba46668fad435c8ae8fee539e1f961", "aa32eeb3e38a44b09f87125be320f1b3", "780bf497ebad453c98d48f8a73c71c4e" ] }, "id": "ocBhbxkrGN5A", "outputId": "b1ddc7c5-10ad-49ab-dc0b-a49771bd7b8f" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Saving model checkpoint to wav2vec2-large-xls-r-300m-pa-in\n", "Configuration saved in wav2vec2-large-xls-r-300m-pa-in/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-pa-in/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-pa-in/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": "115fbb1c21b145618fc0d391e201f922", "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': 'pa-IN'}}\n", "To https://huggingface.co/anuragshas/wav2vec2-large-xls-r-300m-pa-in\n", " 1cdd79d..8e03b20 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-pa-in/commit/1cdd79df0dfa4f2075b214fcad940488dea7edf2'" ] }, "metadata": {}, "execution_count": 42 } ] }, { "cell_type": "code", "source": [ "" ], "metadata": { "id": "2Lyjc1qQHB37" }, "execution_count": null, "outputs": [] } ] }