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"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", 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"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<?, ?B/s]" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7008109b38be4703ade3baf07218c2ac", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Downloading: 0%| | 0.00/2.92k [00:00<?, ?B/s]" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "87a771d299d4434e84095f890220a154", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Downloading: 0%| | 0.00/49.7k [00:00<?, ?B/s]" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Downloading and preparing dataset common_voice/hi to /root/.cache/huggingface/datasets/mozilla-foundation___common_voice/hi/7.0.0/33e08856cfa0d0665e837bcad73ffd920a0bc713ce8c5fffb55dbdf1c084d5ba...\n" ] }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e8860b5c46a5429d8fb7dbd2ac6b23bb", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Downloading: 0%| | 0.00/253M [00:00<?, ?B/s]" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1842990a86c4465c9a3e8bc35b8be6a0", "version_minor": 0, "version_major": 2 }, "text/plain": [ "0 examples [00:00, ? examples/s]" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f7efe021ab9b468885914b0af8f6e2ae", "version_minor": 0, "version_major": 2 }, "text/plain": [ "0 examples [00:00, ? examples/s]" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7994d9cc75bf44c4b1beac50f5b547b5", "version_minor": 0, "version_major": 2 }, "text/plain": [ "0 examples [00:00, ? examples/s]" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "40313c4feb6a40ea9387c22bfef3042a", "version_minor": 0, "version_major": 2 }, "text/plain": [ "0 examples [00:00, ? examples/s]" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6eae98c905d345f5a3b2ea54bb3b64da", "version_minor": 0, "version_major": 2 }, "text/plain": [ "0 examples [00:00, ? examples/s]" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Dataset common_voice downloaded and prepared to /root/.cache/huggingface/datasets/mozilla-foundation___common_voice/hi/7.0.0/33e08856cfa0d0665e837bcad73ffd920a0bc713ce8c5fffb55dbdf1c084d5ba. Subsequent calls will reuse this data.\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "Reusing dataset common_voice (/root/.cache/huggingface/datasets/mozilla-foundation___common_voice/hi/7.0.0/33e08856cfa0d0665e837bcad73ffd920a0bc713ce8c5fffb55dbdf1c084d5ba)\n" ] } ] }, { "cell_type": "code", "source": [ "common_voice_train = common_voice_train.remove_columns([\"accent\", \"age\", \"client_id\", \"down_votes\", \"gender\", \"locale\", \"segment\", \"up_votes\"])\n", "common_voice_test = common_voice_test.remove_columns([\"accent\", \"age\", \"client_id\", \"down_votes\", \"gender\", \"locale\", \"segment\", \"up_votes\"])" ], "metadata": { "id": "k8SSDpswEXSA" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "import re\n", "chars_to_remove_regex = '''[\\।\\!\\\"\\,\\-\\.\\?\\:\\|\\“\\”\\–\\;\\'\\’\\…\\¤]'''\n", "\n", "def remove_special_characters(batch):\n", " batch[\"sentence\"] = re.sub(chars_to_remove_regex, '', batch[\"sentence\"]).lower()\n", " return batch" ], "metadata": { "id": "9MpryC5hEoEN" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "common_voice_train = common_voice_train.map(remove_special_characters)\n", "common_voice_test = common_voice_test.map(remove_special_characters)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "2f46b4c92f304f5d80f100efa71e03d5", "f0e29ecbc70d4ab18325cbf97338e3b6", "a9702d34d44a443582a967a9e5608615", "d3cafd153db54cb0baac7a0760fea874", "313fd572b37748b99f549c9c779d47d8", "7907b6e4326f4c2e9619f90189199830", "7f0a73761d02439799a257a3571e1082", "c10dc265a7e24befbff9cbbd792dff07", "69c86073119c4d52b6844e4a00ccf5e0", "cd25948352f14c018c8242cbe33f02e1", "5808351d40f14920a002d21f24b6d042", "4c4a406050af478a98df256adeb34653", "6e1f8d8513be4a708143076aead86dec", "c87c4c5d72a648b1ad1e00645a6849cc", "07e36c8d90044b87add9804622813331", "98c1402552534c81b75a71dd3117342b", "dd29ced759ad4940a5b1960650d8d0e3", "019cc504e67d4707b3c572b78833de2c", "67fa4826839f47059ccbede01bd381f3", "2390872fe5cc4a6c9db61f681f4f2bd5", "166aa6ba12864ff3b822f3cfe0c85530", "d09f54f5949e45dd90eeb01ab1ff8d74" ] }, "id": "-Zy_4bUtEpyD", "outputId": "5186aa3a-7e61-4f2f-ed0a-dcb0056b22fb" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2f46b4c92f304f5d80f100efa71e03d5", "version_minor": 0, "version_major": 2 }, "text/plain": [ " 0%| | 0/781 [00:00<?, ?ex/s]" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4c4a406050af478a98df256adeb34653", "version_minor": 0, "version_major": 2 }, "text/plain": [ " 0%| | 0/343 [00:00<?, ?ex/s]" ] }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "def extract_all_chars(batch):\n", " all_text = \" \".join(batch[\"sentence\"])\n", " vocab = list(set(all_text))\n", " return {\"vocab\": [vocab], \"all_text\": [all_text]}" ], "metadata": { "id": "6J_JKsIhEsqy" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "vocab_train = common_voice_train.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=common_voice_train.column_names)\n", "vocab_test = common_voice_test.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=common_voice_test.column_names)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "27e5289020704e2ea220aaf4b1c84c6f", "6607f92626ad41efac3e9f9150f1b0d1", "4832fb48499c4191954f6b1cb0c859b6", "df7dca47025644a19e8cbcf50167a705", "a0f37c4bc6f14e869396c5b3c392501f", "80bc99e80f6c4ef993730f35a0d104af", "aeb0c45012eb4933a788dc44dd5d100f", "afc9e61a7da04403b14d6400f39f304f", "f33573b14aea4b4dbe9f569390cafcc4", "e468fb4a6f2e4b80a2ed51c9aa645d9a", "a6250cdf0eba464e9d286d00d99488d4", "8693aaf4f001405292ecbc975476af36", "9977a11f64714072a683ba766713cc20", "ceacb7a9adec4847840b5e3bb0a41fce", "dbfae24655d24f27b6497b26938f3e4d", "81602e6f4d9b4169b69504b6fe58c9da", "a4f46479e23b4799860f59cf8243dc7e", "1b6d8c37e0b04cd8a0e9d2b9aef1aac5", "b5e42b70fe904be7850b29dcdd1bd745", "6b009aae33114c559a927e21e4b8c24f", "900bc5a11fa64d39b71aaabd0e1ba836", "475d0cf270214d1a98f0e77482db774b" ] }, "id": "tZKNfrbPEzNs", "outputId": "99da0b72-9f18-4b32-ab65-7a9b239c27c6" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "27e5289020704e2ea220aaf4b1c84c6f", "version_minor": 0, "version_major": 2 }, "text/plain": [ " 0%| | 0/1 [00:00<?, ?ba/s]" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8693aaf4f001405292ecbc975476af36", "version_minor": 0, "version_major": 2 }, "text/plain": [ " 0%| | 0/1 [00:00<?, ?ba/s]" ] }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "vocab_list = list(set(vocab_train[\"vocab\"][0]) | set(vocab_test[\"vocab\"][0]))" ], "metadata": { "id": "-7INalVeEuxS" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "vocab_dict = {v: k for k, v in enumerate(sorted(vocab_list))}\n", "vocab_dict" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "IM-VpPKoEv97", "outputId": "39f666b4-c44f-40e4-e26e-7a6588a0ad64" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{' ': 0,\n", " 'ਂ': 1,\n", " 'ਅ': 2,\n", " 'ਆ': 3,\n", " 'ਇ': 4,\n", " 'ਈ': 5,\n", " 'ਉ': 6,\n", " 'ਊ': 7,\n", " 'ਏ': 8,\n", " 'ਐ': 9,\n", " 'ਓ': 10,\n", " 'ਔ': 11,\n", " 'ਕ': 12,\n", " 'ਖ': 13,\n", " 'ਗ': 14,\n", " 'ਘ': 15,\n", " 'ਚ': 16,\n", " 'ਛ': 17,\n", " 'ਜ': 18,\n", " 'ਝ': 19,\n", " 'ਟ': 20,\n", " 'ਠ': 21,\n", " 'ਡ': 22,\n", " 'ਢ': 23,\n", " 'ਣ': 24,\n", " 'ਤ': 25,\n", " 'ਥ': 26,\n", " 'ਦ': 27,\n", " 'ਧ': 28,\n", " 'ਨ': 29,\n", " 'ਪ': 30,\n", " 'ਫ': 31,\n", " 'ਬ': 32,\n", " 'ਭ': 33,\n", " 'ਮ': 34,\n", " 'ਯ': 35,\n", " 'ਰ': 36,\n", " 'ਲ': 37,\n", " 'ਲ਼': 38,\n", " 'ਵ': 39,\n", " 'ਸ਼': 40,\n", " 'ਸ': 41,\n", " 'ਹ': 42,\n", " '਼': 43,\n", " 'ਾ': 44,\n", " 'ਿ': 45,\n", " 'ੀ': 46,\n", " 'ੁ': 47,\n", " 'ੂ': 48,\n", " 'ੇ': 49,\n", " 'ੈ': 50,\n", " 'ੋ': 51,\n", " 'ੌ': 52,\n", " '੍': 53,\n", " 'ਖ਼': 54,\n", " 'ਗ਼': 55,\n", " 'ਜ਼': 56,\n", " 'ੜ': 57,\n", " 'ਫ਼': 58,\n", " 'ੰ': 59,\n", " 'ੱ': 60}" ] }, "metadata": {}, "execution_count": 20 } ] }, { "cell_type": "code", "source": [ "vocab_dict[\"|\"] = vocab_dict[\" \"]\n", "del vocab_dict[\" \"]" ], "metadata": { "id": "7b961JX1E1X3" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "vocab_dict[\"[UNK]\"] = len(vocab_dict)\n", "vocab_dict[\"[PAD]\"] = len(vocab_dict)\n", "len(vocab_dict)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "k45hegDKFoFW", "outputId": "c3d74bec-18f2-4e3e-e42c-362de7cd70b5" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "63" ] }, "metadata": {}, "execution_count": 22 } ] }, { "cell_type": "code", "source": [ "import json\n", "with open('vocab.json', 'w') as vocab_file:\n", " json.dump(vocab_dict, vocab_file)" ], "metadata": { "id": "TC7QqjmTFoI2" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from transformers import Wav2Vec2CTCTokenizer\n", "\n", "tokenizer = Wav2Vec2CTCTokenizer.from_pretrained(\"./\", unk_token=\"[UNK]\", pad_token=\"[PAD]\", word_delimiter_token=\"|\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "r4wMI4q8FoLF", "outputId": "1114c379-2d16-46d0-b404-3b996d420741" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "file ./config.json not found\n", "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n" ] } ] }, { "cell_type": "code", "source": [ "repo_name = \"wav2vec2-large-xls-r-300m-pa-in\"" ], "metadata": { "id": "oQm9jF7WFoNc" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "tokenizer.push_to_hub(repo_name)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 160 }, "id": "jOzuCg9bFoPj", "outputId": "5f393d82-8793-46d0-d065-5afc540a9b0a" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.7/dist-packages/huggingface_hub/hf_api.py:1004: FutureWarning: `create_repo` now takes `token` as an optional positional argument. Be sure to adapt your code!\n", " FutureWarning,\n", "Cloning https://huggingface.co/anuragshas/wav2vec2-large-xls-r-300m-pa-in into local empty directory.\n", "To https://huggingface.co/anuragshas/wav2vec2-large-xls-r-300m-pa-in\n", " aa975ff..50e2ea6 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/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<?, ?ex/s]" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "23de9c43f3bb4b51b8195dc6e952c5a6", "version_minor": 0, "version_major": 2 }, "text/plain": [ " 0%| | 0/343 [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": "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": { "application/vnd.jupyter.widget-view+json": { "model_id": "cca666050afa450a830b4180b86fbfa8", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Downloading: 0%| | 0.00/1.91k [00:00<?, ?B/s]" ] }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "import numpy as np" ], "metadata": { "id": "zxSG-t66GAqu" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "def compute_metrics(pred):\n", " pred_logits = pred.predictions\n", " pred_ids = np.argmax(pred_logits, axis=-1)\n", "\n", " pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id\n", "\n", " pred_str = processor.batch_decode(pred_ids)\n", " # we do not want to group tokens when computing the metrics\n", " label_str = processor.batch_decode(pred.label_ids, group_tokens=False)\n", "\n", " wer = wer_metric.compute(predictions=pred_str, references=label_str)\n", "\n", " return {\"wer\": wer}" ], "metadata": { "id": "yjnMNMdrGBK_" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from transformers import Wav2Vec2ForCTC\n", "\n", "model = Wav2Vec2ForCTC.from_pretrained(\n", " \"facebook/wav2vec2-xls-r-300m\", \n", " attention_dropout=0.2,\n", " hidden_dropout=0.05,\n", " feat_proj_dropout=0.05,\n", " mask_time_prob=0.05,\n", " layerdrop=0.04,\n", " ctc_loss_reduction=\"mean\", \n", " pad_token_id=processor.tokenizer.pad_token_id,\n", " vocab_size=len(processor.tokenizer),\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 188, "referenced_widgets": [ "859998a6144b4aa6a40d6b186037aefc", "2f7f650434bb4f85ab3982c979662ffe", "5a4831b1b7504451b9c0140d759db751", "418de958bc194f9a805515e0b0a6c0b8", "0835fd08d2364427bb450a79a8481f95", "f48f2a9c5d50459eaaef0d39273dc88f", "213c46f413de4b3a93297f3955340151", "df4572ed9a5f4168908aec066810a4bf", "0eeed307a9aa467496659a1642fa87cf", "ce45dabe67df448abe2ed48520a4293d", "a5b7a809ff554df084d8eae900467bc7", "0166eefd6d07437799914b69c9b4cf2b", "e02ef345a65b4296b1fd19af4b63c4e4", "1191f40ae4ba4ecf9f7005c543242180", "002043b7b39b482a84c1886bcffa1996", "b74936b55ac540a49ecf5340556fe7b9", "ccfd5c96aab9498fbdc7d39eba3a9e04", "80b1c449181448ef80702e8813f247a4", "58b84e2831124734af25d53bedc1a94d", "a130b3771afd4c2bb99434bd8ccd9da7", "2b98c3c1ada943f898ce9188bbecf10d", "5cb2d2fb1c8349b7a28cd60197e47e45" ] }, "id": "IrIo68UAGCqw", "outputId": "57b59f2c-274e-4657-aa21-e8cacd53f13c" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "859998a6144b4aa6a40d6b186037aefc", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Downloading: 0%| | 0.00/1.53k [00:00<?, ?B/s]" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0166eefd6d07437799914b69c9b4cf2b", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Downloading: 0%| | 0.00/1.18G [00:00<?, ?B/s]" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "Some weights of the model checkpoint at facebook/wav2vec2-xls-r-300m were not used when initializing Wav2Vec2ForCTC: ['quantizer.weight_proj.bias', 'project_hid.weight', 'quantizer.codevectors', 'project_q.weight', 'project_hid.bias', 'quantizer.weight_proj.weight', 'project_q.bias']\n", "- This IS expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", "- This IS NOT expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n", "Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/wav2vec2-xls-r-300m and are newly initialized: ['lm_head.weight', 'lm_head.bias']\n", "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" ] } ] }, { "cell_type": "code", "source": [ "model.freeze_feature_extractor()" ], "metadata": { "id": "Jr5ybAlfGEN9" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from transformers import TrainingArguments\n", "\n", "training_args = TrainingArguments(\n", " output_dir=repo_name,\n", " group_by_length=True,\n", " per_device_train_batch_size=16,\n", " gradient_accumulation_steps=2,\n", " evaluation_strategy=\"steps\",\n", " num_train_epochs=120,\n", " gradient_checkpointing=True,\n", " fp16=True,\n", " save_steps=400,\n", " eval_steps=400,\n", " logging_steps=400,\n", " learning_rate=3e-4,\n", " warmup_ratio=0.12,\n", " save_total_limit=1,\n", " push_to_hub=True,\n", ")" ], "metadata": { "id": "p2MOKEf_GGa6" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from transformers import Trainer\n", "\n", "trainer = Trainer(\n", " model=model,\n", " data_collator=data_collator,\n", " args=training_args,\n", " compute_metrics=compute_metrics,\n", " train_dataset=common_voice_train,\n", " eval_dataset=common_voice_test,\n", " tokenizer=processor.feature_extractor,\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "FoEFkVqAGHz0", "outputId": "af371c0a-c589-484d-c584-2ec2155d4143" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/content/wav2vec2-large-xls-r-300m-pa-in is already a clone of https://huggingface.co/anuragshas/wav2vec2-large-xls-r-300m-pa-in. Make sure you pull the latest changes with `repo.git_pull()`.\n", "Using amp half precision backend\n" ] } ] }, { "cell_type": "code", "source": [ "trainer.train()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "BB0VjkhcGK2H", "outputId": "b06640ae-65f5-4c04-8584-0d72f9a62441" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "The following columns in the training set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running training *****\n", " Num examples = 781\n", " Num Epochs = 120\n", " Instantaneous batch size per device = 16\n", " Total train batch size (w. parallel, distributed & accumulation) = 32\n", " Gradient Accumulation steps = 2\n", " Total optimization steps = 2880\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" ] }, { "output_type": "display_data", "data": { "text/html": [ "\n", " <div>\n", " \n", " <progress value='2880' max='2880' style='width:300px; height:20px; vertical-align: middle;'></progress>\n", " [2880/2880 3:13:48, Epoch 119/120]\n", " </div>\n", " <table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: left;\">\n", " <th>Step</th>\n", " <th>Training Loss</th>\n", " <th>Validation Loss</th>\n", " <th>Wer</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>400</td>\n", " <td>6.480400</td>\n", " <td>1.846061</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <td>800</td>\n", " <td>0.474000</td>\n", " <td>1.101765</td>\n", " <td>0.662428</td>\n", " </tr>\n", " <tr>\n", " <td>1200</td>\n", " <td>0.138900</td>\n", " <td>1.191819</td>\n", " <td>0.610336</td>\n", " </tr>\n", " <tr>\n", " <td>1600</td>\n", " <td>0.091900</td>\n", " <td>1.188941</td>\n", " <td>0.605824</td>\n", " </tr>\n", " <tr>\n", " <td>2000</td>\n", " <td>0.065700</td>\n", " <td>1.226577</td>\n", " <td>0.593109</td>\n", " </tr>\n", " <tr>\n", " <td>2400</td>\n", " <td>0.047900</td>\n", " <td>1.251161</td>\n", " <td>0.590238</td>\n", " </tr>\n", " <tr>\n", " <td>2800</td>\n", " <td>0.035500</td>\n", " <td>1.254808</td>\n", " <td>0.567678</td>\n", " </tr>\n", " </tbody>\n", "</table><p>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "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<?, ?B/s]" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "81557dc72fbb4a18b2207e61acf83968", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Upload file runs/Jan22_00-06-54_7fa4f467060a/events.out.tfevents.1642810066.7fa4f467060a.75.0: 41%|####1 …" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "To https://huggingface.co/anuragshas/wav2vec2-large-xls-r-300m-pa-in\n", " 50e2ea6..1cdd79d main -> 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": [] } ] }