{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "pwqX3mdhXwx5", "outputId": "1911051b-1c69-44ce-8942-72b3e263f16d" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Fri Feb 4 14:06:02 2022 \n", "+-----------------------------------------------------------------------------+\n", "| NVIDIA-SMI 460.32.03 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 41C 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" ] } ], "source": [ "!nvidia-smi" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "5SUbafEAX0CJ" }, "outputs": [], "source": [ "%%capture\n", "!pip install datasets --upgrade\n", "!pip install transformers\n", "!pip install huggingface_hub\n", "!pip install torchaudio\n", "!pip install jiwer" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 388, "referenced_widgets": [ "ec24aff2df5a40b992c22869584e8b4b", "d2011eb5dbee4742befbfd5a82b8178b", "299f5af5a206448b9168a38a7129e16e", "eaa3d96e8f7848e58330746d9ebd6dd3", "fe88c00e1e054ae6ad5e7e46d64f8846", "68b88fad0b8e4c39a41d1c7e6cc3cf30", "cb24d762b6ce4aa190663b92c2dfe794", "5dff493efeeb4ba9b3c76e19c4319176", "277d41ff3f6747e0867a2a657ec0e677", "e803d9f272be4d119d786c769be44069", "8c9b333c889d41bcaa29ddaebf488b75", "7dc97567df304a21beacf24748d92e43", "dc1e468e033f4000b923da839abe5f7f", "51b70e5032e9459896da77ee98b44254", "5bd2af6895f14d8dbb10f551a4116783", "999e03d7f3b74d23b99dfbb66cc16360", "9b310f34d07547c5b6d1d29536c50aaa" ] }, "id": "rWmcNVb2X7Hd", "outputId": "a49e2143-7f24-4e7a-ce83-fb2e168c7f08" }, "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" ] } ], "source": [ "from huggingface_hub import notebook_login\n", "\n", "notebook_login()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "3X_6k7imX9L8" }, "outputs": [], "source": [ "%%capture\n", "!apt install git-lfs" ] }, { "cell_type": "markdown", "metadata": { "id": "pP37N8UPYJ9K" }, "source": [ "#Create Wav2Vec2CTCTokenizer" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 220, "referenced_widgets": [ "a7d75f37d4684cb99c1a195968b23a4d", "76db94cf576748b5908bacafb506463a", "ede2e22a72c648d183c0e0b5a68618d5", "1ee68ec17782438e8ab16c19d93c173e", "0bede2ce80e94e048e5bf216e0985352", "a454b4c7f4c744ff8d90646006bc8cd2", "363be107cfb0407ea8db47e7c6502c81", "c86e676cb57749db81174f06a0245ef7", 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"6997bfd5b344427d8a123bf61f05da0c", "58dbbb2711d1460394013a5f55d57e48", "57276b7212404734930c2b42fbe412fe", "96776ea5f33e4a289e62f294788e5e1c", "d48c2d7c3f0b4280aa938080995d5296", "163722cd083e492ba1a4711c95a76888", "daa1c09d26114c3e8a118872ab39147d", "b2cd5f9b7ecc4efb86f805f2dae504da", "37313f26dfce42f0baeabb7105151dd2", "b9211ab3f40544ea81da94dce96052de" ] }, "id": "O2xxPBqwYH0Z", "outputId": "b384e672-1779-4a84-ed04-b9d3c824991f" }, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a7d75f37d4684cb99c1a195968b23a4d", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Downloading: 0%| | 0.00/10.1k [00:00\n", " \n", " \n", " \n", " sentence\n", " \n", " \n", " \n", " \n", " 0\n", " ହାତରେ ଢାଳଟିଏ ଧରି ପିଠିରେ ସାନ ବୋକଚାଟିଏ ପକାଇ ପଛରେ ଜଣେ ଭଣ୍ଡାରି ଚାଲିଛି ।\n", " \n", " \n", " 1\n", " ମୁଁ ତୁମର ପୁଅ ଜାଣ ।\n", " \n", " \n", " 2\n", " ମୋତେ ଜର ହେଲେ କିଏ ପଥି ରାନ୍ଧି ଖୁଆଇବ, ମୋ ସାଆନ୍ତାଣି ଗୋ!\n", " \n", " \n", " 3\n", " \"ମଙ୍ଗରାଜେ ଆଉ ମେଦିନୀପୁର ନ ଯାଇ କଟକରେ ମାମଲା କରି ଜମିଦାରୀ ଦଖଲ କରିଗଲେ ।\"\"\"\n", " \n", " \n", " 4\n", " ମୌଲବୀ ସାହେବଙ୍କର କିଛି ପରିଚୟ ଦେବାର ଆବଶ୍ୟକ ।\n", " \n", " \n", " 5\n", " ନିଜର ଥୋରି ବଳଦରେ ଲଦି ପିଲାର ମାଲଗୁଡିକ ଆପଣା ଘରକୁ ଘେନି ଆସନ୍ତି ।\n", " \n", " \n", " 6\n", " ଓଡ଼ିଶାରେ ସିଂହାରହାର ପୁଷ୍ପରେ ଏକପ୍ରକାର ରଙ୍ଗ ପ୍ରସ୍ତୁତ ହୁଏ, କିନ୍ତୁ ତାହା ଲାଭଜନକ ନୂହେ ।\n", " \n", " \n", " 7\n", " \"ମୁଁ ଆଉ କିଛି ଜାଣେ ନାହିଁ ।\"\"\"\n", " \n", " \n", " 8\n", " \"ସମସ୍ତଙ୍କର ଦେଖାସାକ୍ଷାତ ନାହିଁ, ଭାରି ଦରକାରି କାମରେ ଏପରି ମଠ କଲେ କି ଚଳେ?\"\"\"\n", " \n", " \n", " 9\n", " ଗାଈ ତ ନୁହେଁ, ଗୋଟିଏ ଗୁଣ୍ଠୁଣୀ ହାତୀ ।\n", " \n", " \n", "" ], "text/plain": [ "" ] }, "metadata": {} } ], "source": [ "show_random_elements(common_voice_train.remove_columns([\"path\", \"audio\"]), num_examples=10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "NRDcDNteYabw" }, "outputs": [], "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" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "dfc08437a8d0440d8f517d543641ed95", "ad750e31f6624c258ff14510316ea97e", "c42cc0614e964946a943bac07f0125e1", "9615850136f340bea6603c75e918f121", "9f9c68ccd56d466caea5ff226d1978ef", "e2aa0d3aee064a7f82ba9cf75034dd96", "1a939b8dbfe1440a9f973937552263a1", "e9088dd889654777a8435383586a830d", "b1d3921c1b1440ad95ba14dd6c554b0c", "cf872a9930ff4fe981614aa6cc16c55a", "ea816ab6dd384e85b5a3b57d0e0c7940", "79bfb77cbb9c455c822eab8c9d931a44", "a993babd1934477da79dd6c118bbf2b8", "9d54f2e696314acf87e7c878f8e81f4e", "91134a31c5c542ca9d1b0cdb88b2a247", "4b0a824d334041e09bed3b271795242f", "b534ba47962049968c3cba758bb3e4fd", "eee2bb6e965d4e528906aa44a4e20e35", "43e10ba5f0934289b5b090843d2b049b", "708b9ac2e1054eac9ace4b750a22b1bd", "b118f9fa436d4e5db547f98629811a0a", "ef9564dd5fcc4559977f93ab8d05a949" ] }, "id": "SyWGPpzNYciF", "outputId": "8b3ed8d5-e700-423b-f1a2-b5a638980392" }, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "dfc08437a8d0440d8f517d543641ed95", "version_minor": 0, "version_major": 2 }, "text/plain": [ "0ex [00:00, ?ex/s]" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "79bfb77cbb9c455c822eab8c9d931a44", "version_minor": 0, "version_major": 2 }, "text/plain": [ "0ex [00:00, ?ex/s]" ] }, "metadata": {} } ], "source": [ "common_voice_train = common_voice_train.map(remove_special_characters)\n", "common_voice_test = common_voice_test.map(remove_special_characters)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 363 }, "id": "5XHPLLvAYeMY", "outputId": "719eaf7a-1c94-499d-e550-1eff54072cc1" }, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
sentence
0ତେବେ କଣ ବୀରେଇ ଅନାଥ ହୋଇଯିବ
1ସେହିବିଡ଼ାଟା ଚାନ୍ଦିଆ ପଲା ମଧ୍ୟରେ ଲୁଚାଇ ରଖିଥାଏ
2ସମସ୍ତେ ଖଣ୍ଡେ ଖଣ୍ଡେ ସୂତା ଶାଢ଼ୀ ଆହୁତି ଦିଅନ୍ତି
3ରୁକୁଣୀ ଚେମୀ ନାକଫୋଡ଼ୀ ଟେରୀ ବିମଳୀ ଶୁକୀ ପାଟକୁ ଛାଡ଼ି ଆହୁରି କେତେଗୁଡ଼ିଏ ପୋଇଲୀ ଅଛନ୍ତି
4ସେମାନଙ୍କର ଜୀବନ୍ତ କବିତା ସର୍ବତ୍ର ଆଦୃତ ହୁଏ
5ପର ଚାଷ ପ୍ରତି ମଧ୍ୟ ସେପରି ଦୃଷ୍ଟି
6ଗ୍ରାମଲୋକମାନେ ଯାତ୍ରାକାଳୀପରି ଛିଡ଼ାହୋଇ ଚାହିଁଅଛନ୍ତି
7ଘରେ ଯାଇ ଯାହା ଦେଖିଲି ଦଣ୍ଡେ ରହିବାକୁ ଇଚ୍ଛା ବଳିଲା ନାହିଁ
8ଭଲଲୋକ କବାଟ ପଛରୁ ଫଁ କରି ଗୋଟିଏ ନିଶ୍ୱାସ ଶବ୍ଦ ଶୁଣିଲେ ପାଦଯୋଡ଼ିକ ଆଉ ଦିଶିଲା ନାହିଁ
9ଆମ୍ଭେମାନେ ଅନୁମାନ କରୁଁ ଜଣେ ମରହଟ୍ଟୀ କବି ଏହି କବିତା କହିଅଛନ୍ତି
" ], "text/plain": [ "" ] }, "metadata": {} } ], "source": [ "show_random_elements(common_voice_train.remove_columns([\"path\",\"audio\"]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "z0UilCejYk66" }, "outputs": [], "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]}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "90f7d745d13e4709b9b5abc0800be77f", "fc022fa1daf34514aa91b157864ea846", "99295700b9c1487fad7b8634b171eea8", "42eaa9839260490a9851d9c8d77aeb7e", "211f3624821643afab2602eb5145f859", "5110671a92844cc785d5a25fe3f83374", "5827c1f1972c439e81190b5bd87955cd", "f3ef57d4a08149acb8527bdedadbf4ed", "29ff7ae235fe41609796a3bd66887c68", "ce8a58ca25424b2db0ee49f9f60fad44", "8c7b1a642a5c498694fe4fa9a31d16d5", "ef41844056fc4a33b37960c454176d2c", "b4a118fcafe64d8da82a5dfd82bc5ee4", "c69c681d46d24687bc251e663dfcf880", "4847dcdec07645639b194650be8b1f92", "64a392bfac564e94a3c9158f12b431a9", "9ed8600f580d4b728aaabe7c0cd905df", "a423fd27a9c94a129e3b8bd83511aabc", "02c599034d7f4077957d075427cb7998", "f018b3b9aa2b46a4bc0ed99180dd39ec", "74f76fe3b62f4b3fb5c6c7fb7703dd05", "fedfa4c1538f438c9692a0c5c587d96e" ] }, "id": "GbPSiLAXYmvK", "outputId": "b68dc7d8-01b3-4a94-de3f-4bd5c879efa4" }, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "90f7d745d13e4709b9b5abc0800be77f", "version_minor": 0, "version_major": 2 }, "text/plain": [ " 0%| | 0/1 [00:00 main\n", "\n" ] }, { "output_type": "execute_result", "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'https://huggingface.co/DrishtiSharma/wav2vec2-large-xls-r-300m-or-d5/commit/0841d8d82f270da3706e27dae6a512ce2ae32928'" ] }, "metadata": {}, "execution_count": 24 } ], "source": [ "tokenizer.push_to_hub(repo_name)" ] }, { "cell_type": "markdown", "metadata": { "id": "9vYsH0F1ZWTa" }, "source": [ "#Create Wav2Vec2FeatureExtractor" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "y_rImGQgZTOf" }, "outputs": [], "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)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "zo4VRjMpZalf" }, "outputs": [], "source": [ "from transformers import Wav2Vec2Processor\n", "\n", "processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 36 }, "id": "C2VeeMJXZcPc", "outputId": "f55d4773-69d5-43a0-b469-dc1e36e2ff1b" }, "outputs": [ { "output_type": "execute_result", "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'cv-corpus-8.0-2022-01-19/or/clips/common_voice_or_21829757.mp3'" ] }, "metadata": {}, "execution_count": 27 } ], "source": [ "common_voice_train[0][\"path\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "0VJbeciEZd53", "outputId": "93e84c60-d531-4a2e-e0d2-fbc8e812598b" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'array': array([ 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n", " 2.5033951e-06, -3.5405159e-05, -3.0219555e-05], dtype=float32),\n", " 'path': 'cv-corpus-8.0-2022-01-19/or/clips/common_voice_or_21829757.mp3',\n", " 'sampling_rate': 48000}" ] }, "metadata": {}, "execution_count": 28 } ], "source": [ "common_voice_train[0][\"audio\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-hNJcREpZfiR" }, "outputs": [], "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))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6HR62lRYZil3", "outputId": "46c02f55-664c-49cf-ed45-1b5dcee234b0" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'array': array([ 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n", " 2.9323280e-06, 9.9777508e-06, -7.2638522e-06], dtype=float32),\n", " 'path': 'cv-corpus-8.0-2022-01-19/or/clips/common_voice_or_21829757.mp3',\n", " 'sampling_rate': 16000}" ] }, "metadata": {}, "execution_count": 30 } ], "source": [ "common_voice_train[0][\"audio\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 93 }, "id": "olI1RRluZkYk", "outputId": "20086a72-5686-48be-95ff-0b8042b02baf" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "ସଞ୍ଜବେଳେ ବାପା ପାଖରେ ବସି ଭଜନ ଗାଇଲେ ଗାଁରେ କୌଣସି କୌଣସି ଲୋକ ଆସି ଶୁଣନ୍ତି \n" ] }, { "output_type": "execute_result", "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "execution_count": 31 } ], "source": [ "import IPython.display as ipd\n", "import numpy as np\n", "import random\n", "\n", "rand_int = random.randint(0, len(common_voice_train)-1)\n", "\n", "print(common_voice_train[rand_int][\"sentence\"])\n", "ipd.Audio(data=common_voice_train[rand_int][\"audio\"][\"array\"], autoplay=True, rate=16000)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "EE5jG9PVZmWU", "outputId": "584be85c-3e58-4f11-8726-a16822254feb" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Target text: ବୁଢୀ ପଚାରିଲା କିଏ ଡାହାଣୀରେ\n", "Input array shape: (56064,)\n", "Sampling rate: 16000\n" ] } ], "source": [ "rand_int = random.randint(0, len(common_voice_train)-1)\n", "\n", "print(\"Target text:\", common_voice_train[rand_int][\"sentence\"])\n", "print(\"Input array shape:\", common_voice_train[rand_int][\"audio\"][\"array\"].shape)\n", "print(\"Sampling rate:\", common_voice_train[rand_int][\"audio\"][\"sampling_rate\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "XobzwvyNZpxv" }, "outputs": [], "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" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "da4dd47985ca4ab686672dcda25f8a38", "cb2ee6af25c648b5955ad2468b17b40c", "34d42ab98fae480c83e7585c1b0126a6", "825379db04fa48e6a00941998893ff07", "1cc391bcbe8b468fbc152ea7dd267e48", "b4770616a7db448a829616d7587811f9", "ab17e13cc50e411b96604a6b00a9f42b", "9795a9c55a234a7c84125418726096f1", "5c652ce95632419db9632f293a746723", "e571d299caf54d3db273a21114ba26e2", "41b20fcbd2204070bb909e53d7809f05", "6382ef3a17034f57889f401ef8ccee75", "fcc3fa155c0e4d81a9de556b2617ec0d", "278c474625244571b604520b6986d5f7", "cde87f70c1444c3d8bda04fc58e86967", "686174f9480f43c7853685a0ed1a0ae0", "0234df9e20d34808b1c8e58a03ed65af", "5dbf4b7d51254cc8a9d5f7084a0aad31", "52adb03ccfa84d2ca892f40e173ccd1c", "2343e99d045f44eea2ed02f09babd2fd", "d22f8ae4dba74506b862358bf13d5e97", "e31f3ed817b943bfa9478f7334d17150" ] }, "id": "A7gT1EQMZsvz", "outputId": "110b660c-9b01-4083-b107-2ac030a61fcf" }, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "da4dd47985ca4ab686672dcda25f8a38", "version_minor": 0, "version_major": 2 }, "text/plain": [ "0ex [00:00, ?ex/s]" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6382ef3a17034f57889f401ef8ccee75", "version_minor": 0, "version_major": 2 }, "text/plain": [ "0ex [00:00, ?ex/s]" ] }, "metadata": {} } ], "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)" ] }, { "cell_type": "markdown", "metadata": { "id": "Hu8imi4QZx3f" }, "source": [ "Training" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "rAYCcrJFZubm" }, "outputs": [], "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" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "k05sM3rsZzGS" }, "outputs": [], "source": [ "data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "6a256ee8367a46b2ab4a37084a705e44", "9b9ef52a6e6b407b89df9018ad53745c", "10deafad31b44ba1abf4095f715999bb", "d9f901b704b740899228c4a6b410ffb8", "89ced185a3f8422f8cfd701464a3d2c3", "31270a7dfee94256940f68cf25b5b773", "c2989ad9aa754bad93cddfdbdbc6c901", "afc6e26e4b504339b94f471ea363d2a5", "c0d7a1f094804906aa22c895233370a1", "05c7aa9d78f5417c8949c5497ed44a1c", "567c4f76ecf6419483d7ef333f23b2ea" ] }, "id": "T1zgf2ZKZ1et", "outputId": "c05b0c2d-af8c-47af-892b-d3e1d71e99d9" }, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { 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StepTraining LossValidation LossWer
3009.2958004.9014191.000000
6003.4065003.5149601.000000
9001.5402000.8355560.724868
12000.6049000.7754360.634921
15000.4074000.7994340.621693
18000.3097000.8814720.598545
21000.2593000.8531560.575397
24000.2097000.9077050.564815
27000.1784000.9047320.566799
30000.1567000.9019090.572751
33000.1315000.9295500.582672
36000.1125000.9255760.568122
39000.1035000.9147730.549603
42000.0901000.9479770.548280
45000.0817000.9798750.551587
48000.0790000.9571460.544974

" ], "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 = 213\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-or-d5/checkpoint-300\n", "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-300/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-300/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-300/preprocessor_config.json\n", "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/preprocessor_config.json\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 213\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-or-d5/checkpoint-600\n", "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-600/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-600/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-600/preprocessor_config.json\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 213\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-or-d5/checkpoint-900\n", "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-900/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-900/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-900/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-or-d5/checkpoint-300] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 213\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-or-d5/checkpoint-1200\n", "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-1200/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-1200/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-1200/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-or-d5/checkpoint-600] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 213\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-or-d5/checkpoint-1500\n", "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-1500/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-1500/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-1500/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-or-d5/checkpoint-900] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 213\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-or-d5/checkpoint-1800\n", "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-1800/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-1800/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-1800/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-or-d5/checkpoint-1200] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 213\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-or-d5/checkpoint-2100\n", "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-2100/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-2100/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-2100/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-or-d5/checkpoint-1500] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 213\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-or-d5/checkpoint-2400\n", "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-2400/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-2400/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-2400/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-or-d5/checkpoint-1800] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 213\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-or-d5/checkpoint-2700\n", "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-2700/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-2700/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-2700/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-or-d5/checkpoint-2100] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 213\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-or-d5/checkpoint-3000\n", "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-3000/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-3000/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-3000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-or-d5/checkpoint-2400] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 213\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-or-d5/checkpoint-3300\n", "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-3300/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-3300/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-3300/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-or-d5/checkpoint-2700] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 213\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-or-d5/checkpoint-3600\n", "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-3600/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-3600/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-3600/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-or-d5/checkpoint-3000] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 213\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-or-d5/checkpoint-3900\n", "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-3900/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-3900/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-3900/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-or-d5/checkpoint-3300] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 213\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-or-d5/checkpoint-4200\n", "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-4200/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-4200/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-4200/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-or-d5/checkpoint-3600] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 213\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-or-d5/checkpoint-4500\n", "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-4500/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-4500/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-4500/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-or-d5/checkpoint-3900] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 213\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-or-d5/checkpoint-4800\n", "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-4800/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-4800/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-or-d5/checkpoint-4800/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-or-d5/checkpoint-4200] due to args.save_total_limit\n", "\n", "\n", "Training completed. Do not forget to share your model on huggingface.co/models =)\n", "\n", "\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=4800, training_loss=1.0604252596696218, metrics={'train_runtime': 27574.3694, 'train_samples_per_second': 5.462, 'train_steps_per_second': 0.174, 'total_flos': 2.5728202399020777e+19, 'train_loss': 1.0604252596696218, 'epoch': 200.0})" ] }, "metadata": {}, "execution_count": 43 } ], "source": [ "trainer.train()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "qKKeV9gEaLHr", "colab": { "base_uri": "https://localhost:8080/", "height": 356, "referenced_widgets": [ "68701b89df614445b0e9d59f5b7c3724", "d2b4a5a81f904b6ea73231eed0c1e84d", "4860a5f3235b47589d52a3049bfee46d", "6f4b0d6b2a7346a99cde6a04412afc62", "94ecfd718bcc480bbd107a010e59a06d", "79e37d42e0dc419e9b4a01fd0f205146", "19803b3ba6f74ac3a584da64a1237465", "871471c65d57421d862a43ebd92f45ca", "2d21fa9bbd264f06aec998430f53450c", 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"output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "68701b89df614445b0e9d59f5b7c3724", "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': 'or'}}\n", "To https://huggingface.co/DrishtiSharma/wav2vec2-large-xls-r-300m-or-d5\n", " a5ccbdf..0cc372e main -> main\n", "\n" ] }, { "output_type": "execute_result", "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'https://huggingface.co/DrishtiSharma/wav2vec2-large-xls-r-300m-or-d5/commit/a5ccbdf2f4b1037067dd1f3f9107c8d94403709c'" ] }, "metadata": {}, "execution_count": 44 } ], "source": [ "trainer.push_to_hub()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "vMeJybu4aPpa" }, "outputs": [], 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