{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "pwqX3mdhXwx5", "outputId": "5da02559-e3df-490b-904f-b2ddc51318f4" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Thu Feb 3 15:59:05 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 27W / 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": [ "39fadf84ded64e32ba0fae9ae504c740", "9de0e393ab34480a98288edcb1e2de0e", "d1a2d09016b546498efc9ec72d1c750f", "b28a162884e54e8bad3261d753650a7c", "fb2f749120094184980a564649ffdab0", "38ebe9b067f847de90a07f39d0c3c98c", "311b5be02a6b4201baa35709aef45223", "17f0ef0c67a14224b950946bd883e3a4", "de269eee29954bcd85dde11edabe32b3", "d74b543cc1124939aeb0b5ec2cec49ca", "061863ca9a264b3b97251f7d70ff355b", "61ea9728aa804ccaa7b91c78152cd981", "d754bf96725e499dbffedf6efd504ae6", "76c674c0c3e84caf94146b63e3b00ffe", "730588def524406fa728e7b793d03f5e", "c6a91aa7ba934142bd5151caf7b7d95d", "d9a76cd1b0ab4497a305f783b2809b27" ] }, "id": "rWmcNVb2X7Hd", "outputId": "65939f47-476f-4a76-c25f-cb25f7982478" }, "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": [ "9d483e020adf4ee98d3eb93396fdf1e1", "a3ca56298476482dadb68bb99523818a", "4ece928827fe4eb5b4643fb54de870f1", "4915b3156f374f329c9a5b19b428ddd9", "8d5cfdd2fc1d4ba08cfbd78f9a412a82", "b964d0c947cc483eb411b1fc381e872e", "2d5157f262b94c64bc92e49d7d233100", "5edb30baefd240f0be22a562a7edf50a", "b636444cbe19415780c583711356c556", "32dae99eb8154f4e81e86811b164edf4", "0a1691ad47a640fc9317bd6770cfbb85", "e21f5be551cf494daa7c91d4f70a8fce", "c3fc6e95bb8c4a84ad2062dc89372990", "033b9923d839486598a700f76fc36c2c", "75889ff4d0eb4faf8a39a98a8efb0d91", "26fe7f22d22046da87ba6cf084d2d06d", "fe153262ef344800a1b924de0851e222", "665dbc3fb33c4ed8aaaaa6df38ecc295", "bf6893afe2894776b98937928a0577d7", "345d44b2bf6f4802a9b96f8081acfd70", "0d56f0ada3c74684a159bc5746b89ef8", "e06acabd36654e5683142f1cfea4a3cf", "297fa5544cc640a6b463e8559b7b4b6b", "21b65720d82045b9861f6d831737542f", "ced73b04ecac4094ad71adf09c34cc37", "aa1d1a8bd8154d0090d5a890b76b09a2", "74cafe7cfe99403188d071223b800b45", "a206ad1077484a5183dacf678b03935f", "0eaa0e705c1d431ea435e0470df9cd8d", "2afcc3eb391945209b0ff1f8bffd3e97", "80e547b161674c8e8be76f0bdd29e1a8", "66db58dbbd19439483ba2b10ce3ab3db", "04e7d18976a74abaa51661dd7644dc6f", "dbe1f676f7c5492c83aa887a0ecca5d4", "1ad9c12e38de4009afec26cdf00b2fd0", "1d9e874bfab34843b3c195d6e795329c", "b89498330f3d4ca48e3bd4a2c1e96ce5", "5674d4833f3942088b51d2f915ff8e84", "e646759b8a3a438b90e51cf23ae901ca", "3922f95e5ab04e6ebf15fcabc7cb27c3", "1c658a9cd076426e9d00efcc5c82744d", "1529dc450b3f40e5aa46e6e7a52913a5", "bccce81df9e348239c05b4e144fbf292", "51ea0d2ee31f45c1b64e6e3a954c91f8", "0472dea318a240dbb59ab33baa3ba9bb", "e24cd9a0ff5e4dceb7dfada9a97823b3", "3708ab3ee0e74831ac7924c22bd934dd", "5cb33d43df3f4464a9d37cfb888d1c8e", "36571259aa794f2e96785a966295f0de", "9eca37b02bd4410fbf9d01520c8539f2", "cbb084cc181347b987f33da11a9644c1", "06788928148347d6bc1a44d2a5eefb40", "25423fc1663c49e18b95811dec0b3fff", "ee154529b56b40b89b35f2cdcea8b6bd", "0270930e2c6641cdbe0b85e9bc5aa59e", "958de68199424e01a6356371b827d83c", "befa27c0495848f8aa091e9279f4785b", "6e5ce37193c0475485b6aabdfa303da2", "649cea92a21e484c960cc76fc6093413", "2d19fa45497f40c79aac16fc635a3870", "3c6c60190aa340cab3a765bc9e7c964a", "1a46f8d6948846398b84ce08a705dd52", "26510ec9c3c3468b9667f1e0079625f6", "4b669d8eca644f9da3e3c14ec99728ac", "48e73efd340949778aad1e5fb91d70dd", "22a4753c05604f1fad1cac24b4e1bcff", "83ec257f2b144755b74e30b041474ea4", "7cb73d4516c94fb287258fb91c47879d", "afab2f2476714750810edda15bfb8fb0", "53082d7575ba44839d23cd48563b7d1c", "ceb370d27f95431eb04039a512b9d6de", "bad2df104da84ba9b8580562254e46e6", "bd93d57258e54add8c4ea2aefb3aed8f", "0dea39fd06be4d7db7c9ca4c427a06bf", "61c8adce17094230ae6e092662a0baa1", "3724f1b4d82d4501acc3ad7b44ca6443", "c4f64e69bfa1424c94c501ef5b0373c2", "b1c52feead7b4b93aafdf58427e25583", "3be94a7508bd4f629a1a1a612e1e63a8", "99b4eefb5e2b43a3a62e8466d3efd0fe", "129952934c024871b276e8163687271b", "dc2dd40b5b8544e0bc919a07ec55c4e5", "d98ae579b52249098b8777745e31e896", "e8c1272d4caf472a915e7ddc903c0775", "f09dd103a34d4f338bf6991088725118", "ccd9d25a40e444cda0d413aa4baf3dc5", "a75772ec7d684193a640a60d08030128", "89ec11da32754e90a997d1932ef3f55c", "27a1cc260c37457884e4cd08458e3fff", "5c18f79721b84ecf808a0a1c1004ed44", "b3db4135ef7642e29d54aa953718dce7", "a8d504bf77304ec498a8796213818ac6", "fbff68fb33c149349e826de0a0925b75", "ac60b559cdd6472db8d71dab52966dd7", "86fcf87105a849899e4fbca23a61d309", "eb5dd5c90ff0423a8fde399c4a2e45c4", "9d770322b75e48ec8055ecbf1b24f1a6", "6db6499515834d3cb192ef593de85c5b", "3531a1877a4645bba62894415140b5ee" ] }, "id": "O2xxPBqwYH0Z", "outputId": "5fe503d6-d58d-43b4-9c5e-63b5cb2023df" }, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9d483e020adf4ee98d3eb93396fdf1e1", "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", " anvioù-bihan hag anv ar gwaz nevez.\n", " \n", " \n", " 1\n", " bremañ e pesketit.\n", " \n", " \n", " 2\n", " \"sec'hed he deus.\"\n", " \n", " \n", " 3\n", " Efed implij ar yezh gant ar strollegezhioù war ar mod ma vez gwelet o yezh gant ar vrezhonegerien, ha gant ar Vretoned na ouzont ket brezhoneg.\n", " \n", " \n", " 4\n", " \"Gwashoc'h c'hoazh !\"\n", " \n", " \n", " 5\n", " \"n'eus ket anv e taolfe e droad amañ.\"\n", " \n", " \n", " 6\n", " \"Kasit ac'hanon d'ar Mariott Hotel.\"\n", " \n", " \n", " 7\n", " Dale am bo.\n", " \n", " \n", " 8\n", " em glin.\n", " \n", " \n", " 9\n", " \"nevez-flamm eo va c'harr-tan.\"\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": [ "365c3d771dde438fb07f2e34bd0728e7", "a940dc3df1054f7086b78efe9f3cb717", "f91dfcf5a8be4b0183cfba4a2afb1971", "76669691c0ff4d41a6be5a6bdf9694eb", "a014c5cf000441b28c49947f7ad67a3d", "1e5b86384f914df09c93b93e13f5d22e", "1db391d2b3bc4745a1641faf6234bd07", "8e272e2631994480afcb6eb01cd5acf1", "bf6dba4a758f4b4d8cf44bae5a5c581f", "e449275721d9418283e0bf44ff0b94f6", "aadda6e400d24235a4fb32eee0c43117", "4d0e51bf8575476c993f9b2da19f76ae", "8402fa52eb324892b091dc0d71019158", "2000818ebda849a7addee42e0f20a44b", "6de5553555824ea5b4e35f3a1a6c6a83", "a794095cb48643588aef8a8f4495b60e", "3e79c0bd5662466ea74e5d12a798579f", "c35396de348a4300ad974e54b8d4adc0", "03ee818520f24e5c96847099bdb5dbd2", "31ae7d680d7f4dc982b02eb89439d28d", "c127dce74018453cadc7c6424089514c", "c00f1c72c55c4f91ab36b8ba2c0c0cf4" ] }, "id": "SyWGPpzNYciF", "outputId": "06b604bb-5ff2-4ae8-9afb-21b654ccc0b6" }, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "365c3d771dde438fb07f2e34bd0728e7", "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": "4d0e51bf8575476c993f9b2da19f76ae", "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": "628e508e-cfa7-49fe-cd29-6214f50f056d" }, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", 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sentence
0ya vat
1chwitellat a ra en ur labourat
2chwech kador vihan
3neus ket dour
4redek evit reiñ lusk dar yezh
5ober a ra penn mouzh
6… eo ma anv
7ma nhallez ket choari
8daou re zivaskell he deus an nadozaer
9ouzh peseurt tarzh roadennoù hocʼh eus cʼhoant da gevreañ
" ], "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": [ "4c78d99ed83a40589f9952064a73e053", "c1772cdef1aa4caea99dbd5d72be3fee", "49d1600d176a4c958af0e78a10bebe1f", "cde1cdab5347484a99fea7d4615bc2fe", "06b683b8d8254015817bc226521937ed", "05554d7ec68a4c5c9e4fb34ac7a80033", "5b420fc5d442428f929fc53217568d2e", "dfd016aaaa064cdea45bcf182a4240fd", "69ee571a17c6470b8e29ac161d96b36c", "63f06c3ed1a040888b02bd21b4705bdb", "68be8ea9bbbb4d1f9e0b245200be8f0a", "752fa5ba38754ac5b23801ba1bf24039", "c33cf5bf795742888451c78808cda58f", "ee6efcee51e34e7ebf044a70339d1932", "525276b93ed74696994bcc8ea890d924", "b2ecd0a19a1140b39c6e952690f3f632", "7ffe52c71f674961aa33f23bde844e13", "08e15ae6c6924a9da859f841692fe732", "b26367248df44aae873150d986479c50", "97f588fa07c94002b327e82d468b8302", "83550f52f91a4659bf0644ad7c7a4a70", "418f874ae2fb4e5b991a8c276b8110f0" ] }, "id": "GbPSiLAXYmvK", "outputId": "f1267d56-cd6a-4165-ff6b-c3ce17894770" }, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4c78d99ed83a40589f9952064a73e053", "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-br-d2/commit/b65d7219f731de2203eecac17fbd086cd8fae554'" ] }, "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": "bc91f091-564a-422e-a8b0-7185dabc7e25" }, "outputs": [ { "output_type": "execute_result", "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'cv-corpus-8.0-2022-01-19/br/clips/common_voice_br_17333681.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": "29926813-4b44-42c1-bd1e-43fa0c52cc30" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'array': array([ 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n", " -4.9769878e-05, -3.3795834e-05, 5.3048134e-06], dtype=float32),\n", " 'path': 'cv-corpus-8.0-2022-01-19/br/clips/common_voice_br_17333681.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": "d681bad5-2dba-42a9-e622-04be1c380017" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'array': array([ 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,\n", " -3.7581591e-05, -5.6451900e-05, -4.3939723e-05], dtype=float32),\n", " 'path': 'cv-corpus-8.0-2022-01-19/br/clips/common_voice_br_17333681.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": "bcd6c3fa-5f2b-4eaa-f7fe-39d6f9776775" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "dre ma faot\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": "fb86bfb5-c22e-4fb0-c4d8-f31510220b5f" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Target text: mat eo an holl\n", "Input array shape: (29184,)\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": [ "fa0cb6eb3e0047a6b521c8ebc85ebf0a", "64f4d8548b454fc49a051f1cc17ad215", "0857a6d453e747dc9219510c0e937111", "cd8e34ed04da43de8784956bbddb214a", "2fbf28b3f44e48718f017e75f78159aa", "cd041625b75c4e2ebefbaa4955ea2f4d", "41b5421e7ded4bc68413021b49e3f457", "e3fc96cd278c43a48edf1b53e661b88d", "366b875600424fef8d5eca183f5c5db5", "dfb75a16170b4fb6b8ec3695b5828d69", "92415e896e8245fc954bc2a70c2135af", "a019444372314e1c9ca2752a5f90b6d8", "b60e91863d6549eba86f28bda9731324", "809899a8619046678dac9d2e833f6916", "9ed9d4eedd7f426bb5e4cab2d42cf9c8", "71cadab4424048dbb783297aaaf2c943", "00b201849a9e413b9801d9c44bc5519b", "8ba251908727417e88b842f0db502bf9", "be80effd3dbf4e9693a358937338f283", "e8fa7ab09d7a4b0eab153734c6f6e576", "1bf12ebcfaf64341a1331b5bc4fbf8d6", "0899a2c108c24ac1a3d1d7818d462e49" ] }, "id": "A7gT1EQMZsvz", "outputId": "c839172b-4ba3-461a-ba41-267ec849577c" }, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "fa0cb6eb3e0047a6b521c8ebc85ebf0a", "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": "a019444372314e1c9ca2752a5f90b6d8", "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": [ "b1f9950cc1014614a4bc26999488c80b", "f8d25a96c73c4efda52f34056d43a30e", "dda2dbd4792947709d4cdbdd94458a79", "dff44a4077964988adafdb6dc86f3769", "bb131a315ed04f75a99ecd94c283ee3e", "0d7df4119b8a42ea940de05376c04ff1", "533265e47dd54713bfe3693e6911fabb", "6697485c34b146248998fb161f6caf66", "e864b6e21b2e4949a67ae1319210b6e5", "b1577fc611cd4f56995ca186dcc40be0", "5e8b10bdaa124fc0bbd3ea8f47f84248" ] }, "id": "T1zgf2ZKZ1et", "outputId": "29adbbf5-bb17-45b0-b1d2-893d6b51f4c3" }, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b1f9950cc1014614a4bc26999488c80b", 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StepTraining LossValidation LossWer
10013.2579005.4250551.000000
2003.8341003.2413491.000000
3003.0659003.0152891.000000
4002.5976001.6643360.965399
5001.3209001.1996320.830530
6001.0514001.0586050.774517
7000.8452001.0553700.763812
8000.7434001.0390970.730071
9000.6983000.9416230.662302
10000.5890000.9367970.654464
11000.5113000.9789240.643567
12000.5014000.9714630.617282
13000.4272000.9449810.616517
14000.3762000.9447480.630185
15000.3793001.0065780.598738
16000.3683000.9236940.600746
17000.3197001.0511320.622252
18000.3181001.0268350.605429
19000.2903001.0645260.581438
20000.2812001.0318650.585357
21000.2805001.0359070.588415
22000.2503001.0327330.572357

" ], "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 = 2147\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-100\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-100/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-100/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-100/preprocessor_config.json\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/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 = 2147\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-200\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-200/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-200/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-200/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 = 2147\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-300\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-300/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-300/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-300/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/checkpoint-100] 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 = 2147\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-400\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-400/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-400/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-400/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/checkpoint-200] 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 = 2147\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-500\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-500/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-500/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-500/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/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 = 2147\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-600\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-600/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-600/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-600/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/checkpoint-400] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 2147\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-700\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-700/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-700/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-700/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/checkpoint-500] 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 = 2147\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-800\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-800/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-800/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-800/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/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 = 2147\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-900\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-900/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-900/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-900/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/checkpoint-700] 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 = 2147\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-1000\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1000/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1000/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/checkpoint-800] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 2147\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-1100\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1100/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1100/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1100/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/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 = 2147\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-1200\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1200/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1200/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1200/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/checkpoint-1000] 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 = 2147\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-1300\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1300/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1300/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1300/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/checkpoint-1100] 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 = 2147\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-1400\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1400/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1400/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1400/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/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 = 2147\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-1500\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1500/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1500/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1500/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/checkpoint-1300] 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 = 2147\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-1600\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1600/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1600/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1600/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/checkpoint-1400] 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 = 2147\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-1700\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1700/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1700/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1700/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/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 = 2147\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-1800\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1800/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1800/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1800/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/checkpoint-1600] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n", "***** Running Evaluation *****\n", " Num examples = 2147\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-1900\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1900/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1900/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-1900/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/checkpoint-1700] 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 = 2147\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-2000\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-2000/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-2000/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-2000/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/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 = 2147\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-2100\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-2100/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-2100/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-2100/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/checkpoint-1900] 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 = 2147\n", " Batch size = 8\n", "Saving model checkpoint to wav2vec2-large-xls-r-300m-br-d2/checkpoint-2200\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-2200/config.json\n", "Model weights saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-2200/pytorch_model.bin\n", "Configuration saved in wav2vec2-large-xls-r-300m-br-d2/checkpoint-2200/preprocessor_config.json\n", "Deleting older checkpoint [wav2vec2-large-xls-r-300m-br-d2/checkpoint-2000] due to args.save_total_limit\n" ] } ], "source": [ "trainer.train()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "qKKeV9gEaLHr" }, "outputs": [], "source": [ "trainer.push_to_hub()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "vMeJybu4aPpa" }, "outputs": [], "source": [ "" ] } ], "metadata": { "accelerator": "GPU", "colab": { "background_execution": 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