{ "cells": [ { "cell_type": "markdown", "id": "5cefac89", "metadata": {}, "source": [ "# Finetuning Whisper-large-V2 on Colab using PEFT-Lora + BNB INT8 training" ] }, { "cell_type": "markdown", "id": "090fa3ed", "metadata": {}, "source": [ "In this Colab, we present a step-by-step guide on how to fine-tune Whisper for any multilingual ASR dataset using Hugging Face ЁЯдЧ Transformers and ЁЯдЧ PEFT. Using ЁЯдЧ PEFT and `bitsandbytes`, you can train the `whisper-large-v2` seamlessly on a colab with T4 GPU (16 GB VRAM). In this notebook, with most parts from [fine_tune_whisper.ipynb](https://colab.research.google.com/github/sanchit-gandhi/notebooks/blob/main/fine_tune_whisper.ipynb#scrollTo=BRdrdFIeU78w) is adapted to train using PEFT LoRA+BNB INT8.\n", "\n", "For more details on model, datasets and metrics, refer blog [Fine-Tune Whisper For Multilingual ASR with ЁЯдЧ Transformers](https://huggingface.co/blog/fine-tune-whisper)\n", "\n" ] }, { "cell_type": "markdown", "id": "625e47a0", "metadata": {}, "source": [ "## Inital Setup" ] }, { "cell_type": "code", "execution_count": null, "id": "eJrPyQM5Xhv5", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "eJrPyQM5Xhv5", "outputId": "cfd6d8c9-964c-492b-b641-8e80e337f783" }, "outputs": [], "source": [ "!add-apt-repository -y ppa:jonathonf/ffmpeg-4\n", "!apt update\n", "!apt install -y ffmpeg" ] }, { "cell_type": "code", "execution_count": null, "id": "r_Ivl7qlX0dz", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "r_Ivl7qlX0dz", "outputId": "2caa9eed-f01a-4603-a527-fe3b0b58b6e2" }, "outputs": [], "source": [ "!pip install datasets>=2.6.1\n", "!pip install git+https://github.com/huggingface/transformers\n", "!pip install librosa\n", "!pip install evaluate>=0.30\n", "!pip install jiwer\n", "!pip install gradio\n", "!pip install -q bitsandbytes datasets accelerate\n", "!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git@main" ] }, { "cell_type": "markdown", "id": "8a528c1a", "metadata": {}, "source": [ "Linking the notebook to the Hub is straightforward - it simply requires entering your Hub authentication token when prompted. Find your Hub authentication token [here](https://huggingface.co/settings/tokens):" ] }, { "cell_type": "code", "execution_count": null, "id": "ed0OpduhX2JF", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 303, "referenced_widgets": [ "c60690c2aee74763bf23115553f4e640", "7d3d6c198e794219ab5db59f0228c8ab", "5d5ea0207c6148769ad9f15b7b3dd92d", "642e28d258ca4c30a5df94c5cf7e0471", "170ee581427d4f30925dc393d124c1be", "d5d5aa24182a4e04b3fdae1ca7fad52a", "6dba643113a547ac9b6e121d008791d6", "cd9bda1053a14890ad9091c63c0a0acf", "4522666ebbcf4647b06ce81a6316fbbb", "989b3df296504f34a62d31ca0d6d88bb", "e8a7a34c6fb146f0b38a40f389a617fa", "960553d142c446cd8852523887a5cc04", "441820fb176048109e0f8f7e9519d735", "0bb38c654e18429a8396466ebab84504", "1acfc4a2809e41dd995817c3526650dd", "6d5801774beb4b529b227ef2f098614e", "dfdf23cde48c421caebb573060641d6a" ] }, "id": "ed0OpduhX2JF", "outputId": "ecc2048a-b46a-4b20-b94a-5912924feb3d" }, "outputs": [], "source": [ "from huggingface_hub import notebook_login\n", "\n", "notebook_login()" ] }, { "cell_type": "code", "execution_count": null, "id": "e1da5fff", "metadata": { "id": "e1da5fff" }, "outputs": [], "source": [ "# Select CUDA device index\n", "import os\n", "\n", "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n", "model_name_or_path = \"openai/whisper-large-v2\"\n", "language = \"Marathi\"\n", "language_abbr = \"mr\"\n", "task = \"transcribe\"\n", "dataset_name = \"mozilla-foundation/common_voice_11_0\"" ] }, { "cell_type": "markdown", "id": "805b1c56", "metadata": {}, "source": [ "## Load Dataset" ] }, { "cell_type": "code", "execution_count": null, "id": "a2787582-554f-44ce-9f38-4180a5ed6b44", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "020176f5aa0a4d489d022ef5e41ef3f6", "cbbba08d9e634560a6c0429e32166fe5", "6d9d1609edc8471dafe653e4da32eeeb", "435b5708e6cf487ebb1799c7b64a8218", "931b96b21b39482298f40449424fdd34", "79da882ea494477a8c94fdac7acd644e", "f14847261aa247fb9561373e0495f3e5", "ce8f306e745d4b158c58058d471de037", "0bd122731d674cdab803a9981eb28237", "46211764bc6641b4b693c16c90747021", "f1884e5a392941bfa8c484496d87084c", "6b1cdae6f5e34d1d9a0b5e7adf5183db", "4d29aa0885214a2aa79701cc226edaef", "5411f018d4464e839f2fb21ad3f026aa", "5fb8e97b25b44e0dae9715eeb97acc5d", "783d4d18627646648f8c120b728babe1", "1a721ed289684104b0fbdc8147c2311b", "4880366554ad4f6687a25b5a17877dcf", "283f5528547b457698ced126c25c2a44", "7c90db94a11e4a5aa432e664b2af4e7e", "7f6a466819bb45e880eb989f388a7523", "d96245da43944c4b8235e0cd02c1aa4c", "41187d9a120448fab6c8608f226971fa", "1d4bd11921d145c7bfd1a8ce0700a666", "2853a43036244f54b74db99311bc580d", "d9a85d7c76b54199bbf7646448e3458c", "b09d153958cf4a28baad268bbda78236", "003227997471488dae9ae26dcbff89ea", "deb18822d58b4b60bb75460f0a5fe921", "e6cf97ef7bc541d0b9c6de206a3a45b0", "a4b16b5279504dd694090798f5925d65", "564cd321c06440e9856f10f5c40c20be", "8a91574c4b6e4745b2b65885323b4d25", "9202065d8e6f425d88e4514dde70992d", "f7b4ec74e2ac45bbbf0265d0363f4d9f", "3a083523ae604362901e4e31c39fe949", "d1bc2ce48c3e481b9059e882b5102946", "30c688df949042ce89337643f6178230", "2bc85a5bde9a454990d3bb7de5e3c7c1", "2ab45b22ce3f400a81cc451b9d7c9eb8", "cdd08679c28642a184805912c07b324e", "90744e5529c04f18b11e00326649abe5", "11b9c50e720a466aa92c64b254d40778", "8f856d6bde4041149324e1f53d19c1cb", "49545bfc91c848b8a465e13d0b45fa34", "45a85ceb24ea444ab18a6985884be966", "ac9c1141ca7c453f84a8ab62b2de9158", "bd4e3eb14252470d9c8ac60a32948a72", "75dfc66dab48405b85073317c8dff155", "b16e3a75acd0403f865849f1de6ca654", "78fb0d05929849ad9aa7a7ded63c7b6b", "3fd0fd7cbbef4785b360891c12017f48", "a9c132959dae4303bcf6015106ce3453", "2ef66808d51d440b9998064e212df420", "45478f2ce991441c8ceaef9acf745084", "c1f019686c564cca87c240a75ab71ad3", "54589117bf244027ba024ea85bd1fd77", "7b7986ad93f64956b8d198d2cb4acb60", "dbc1a016a69b4ad7811d2701a9520a2f", "72f8e7de2a5d4155a9e9146f12e14b19", "46e44f07b96d4c32bbe75bb89159f093", "90b53e96b3f04c2993969b17547ae0d5", "8843f70cfa1f45299a588e96e9c1159a", "8b2e2c650f4b4bee9e763b7c59525ec8", "fb9f013a6188463fad6db70702576c37", "93e2efbb5da747d4b94c916153ee9706", "c44f472624d84e16a0f380580d36ca61", "65188c1fdba2421ba85c2cc349709600", "0683cbffd97a4b75bd5e00a05d541fef", "4573606592ce4b2a914ed0a70b69f9af", "efb85d003fb54f55aa4eadf2ab8b1684", "a235cb3d4d424efeb30901f63fc1dbe5", "2452ab9f6f974d1d831f7c81e884d00e", "bd7da2671a22431889fbfa2ae1e0fe2c", "47ee1eec97cf4af58ed4f50944386a7e", "a05426c8e5f849b7a972dc0df3cd84ef", "1e8cdf737c93431e841e129abb541e22", "91fc5846a32f44d5bc5fa30fdcb3c638", "daf4005d1d334608846d0fb2fe4f837a", "ba48d89cab9346ed92dc338779f9f828", "6b0e0694d895445c9bf7c955a116953f", "ecb7ffba323743c68961e294e74b337b", "460be80f176849e0b1241e3a4fc18b74", "74e2bcef1ce94234bbf6ba0d6488279d", "73bde731e20d48de8ce66b9d72fb95cb", "9afdb23ba9ee47709f194e0e92de0edf", "621f989f46f84d73b96a37c954e92b8d", "89bb7bbcaa194a23bf3d908a4782ccb8", "59e79d9132964429a2a2abbdf4bdbf32", "2e5c1a371742446e8bc416d4735c9ce3", "e57b15cc74e7474083e87722d6acde47", "e899060f1edc43b980fe6f3bbe13c609", "a6963ce72cb3425791804abf1718ba90", "41a7cc33e0bc4bc5bc04dfc72bab7a81", "28279812acfb4272a1c28ed70aa1362d", "451d3851e29e4efabbc2c235dea718da", "18dfdd09a3af49f5b17cda27872d0ba3", "aaeec2b7986d493e8d238aa14f2e5937", "0894264041854eea960707529f3fb8c7", "3a4965f422f14e1ca2a63e1852235507", "e590170f306347f3a82b76a25d37b652", "384f4d9515d1431589a3cc934bcf5ea7", "edc24ce2510f45f8adde0a187016259f", "c2c2608dd091493795d975f1a3cc3762", "1dedb58d31dd43db96ddd7315bb7e2ee", "308e1ee4593b454a84681bda10921207", "8f52bab9ebd049d6a57686d621f407ac", "2958a33cff794de1aa8128327bf405f1", "1b2780d8137042449bd6779c70bf43ca", "ddc2a8e8ef4d429f95081c4c5baf1fb3", "ce06b2a0de6c4fb8bae36bc4d7f63270", "c4cca1778f314ce582bd09b9b2494f82", "3bd70d937e924f61b943acb0aaf15619", "c81c5d3a4dc5409e95a6410e67fa9857", "dd02d1b31bfd4b19ad626d6691a9b293", "b1d780c721b840d7a06661a7a5e63236", "d19402df47464044b36fb5ee4a0c1c4d", "b1221f4e0a57482682ea4bd6ae245da3", "64533507b97148008548e55623b6c2a3", "33888a5cafe6495782309dae44531dd3", "7d5ff2f1b8794bad8286e90307bd6a61", "7a2d45b371ba47a994e4372437f51cff", "dec9f287435e4c6b9fb1d1ead2ded576", "db5e1bf1871546408f233f0cbc37b136", "af44aa66372a43beb9812ad9895d8d1f", "9b427631384c47ab89ee1352c0236afd", "00071f8cf276478fb2740684552f1275", "64378b1064dc4036a9a4c8813013e210", "ed7daa32c94648d5951876229f9835b3", "eb883a37a9cf4945bd864decb4fc87ea", "7b190bb2bd234b7997ca041baddd511f", "7549fd0b38364d8580f8eb1549558a33", "5cba5542df344d54bb80dcf7be56b2ae", "8daed385e6564c7897fb6553669a9065", "25fcd4b584a143058266f7f3a650d69a", "58a15b8df2d54f89828bcd205d6bf298", "8b51633b2fe5479db0fc73cd3f0ebaea", "d2c1704e34c34d12b99c31d64fce88cc", "6bdce3b33872457ea67a3a3191f438ca", "c8ba787279ea43aa97b134c134d4f183", "6efc3be410ef4710a59dea8dbdabcdf3", "6f9d8dc63c494c76b36074af773330ce", "64c0bac85ee446e284ed85ec0eb5ad44", "5162987085ce45d88233f34ca3a41ce0", "00819c11ea23467689e78a00d89d1b09", "65dbf3c3a80b468aa8717e97c14db6a2", "8cb910ad08024c818b99c2e30e30b039", "0235efbdc6a74cce8050ab1116466427", "ee1ae17fdf4143ae8125ce5e2a7e9066", "481ebdff3ccc40fcb7a8d848b01ae8db", "6117f9e9718c4f4388bd1feec34578b4", "623d0c6427964291b5c74fb18bfb4ce4", "14bca5cf764d4464a186961ccc6bdb3d", "75c793c91b9e4aa994c2d34ebb16f7d4", "3502b5f8aeab4bfd89c83b99ab308b9e", "c43169085f2949f1b8259cd0b767d121", "4903414a865c4813a9371e8b301f1af7", "ec7e7e3811e34b4a8c8cc31cd021cf20", "a22db8523d44457bb96448d08129988f", "9980bca9b1334893bf6583c325122f50", "931fc769fbeb42bf82df6ef5f914bf48", "e944fa694d824042845364bdba72d642", "3785471f614f47c78e3000a303b11ca6", "dd634585d5e64c97881b132b1d59083e", "785f3df156b946449c492f1296656a70", "47a25de2e10d4b55a9e6827b85e49c3a", "898c7b43a5e94601bc093e0cde28c2ec", "a03430c0cdcb47bfbd2ffd754074d692", "cda6af0ca01d4053bcebe06f3c41d887", "abe561c67f1f42b29c33d4c296221b2f", "21ca204b93994e8790c9eb7b0722761f", "f628cb62f5fa446eb608467c1ecea526", "e20264d19e804f9dba3e3867ef9b31bd", "43141dcab2324fc6a12f9b8198e10154", "f773a61b1dba4e3eb0df36162efe9abc", "bfc9036c9c5f4be7ab191b92d92f1352", "0f534e081ec64883a5f88d06f93aeb00", "3a94afa1e03544f68df7752a4f503fbc", "32ed17ecaf7548d5a80b27eb97cd78af", "56817cd3b11e46f187dc13e71b7ec97d", "3d30c20af23e495999646521d39e8e66", "bafada4a1f29442296c47018dcaedb77", "6cd3baac550741ba866ce2cd2dcaebdf", "bc9c3ba4bc6d4623a764d92890536935", "719f2b2ab9eb4e348ae902d063c27e2d", "5e09bdca5081429ab82b488a3e016fbf", "200bc7d8c2bc494399a4560dabd64293", "91c3b5ddf5fb4c7aa835d992b4c7b4e7", "52ff6528abdd4a9e9b85f4b355b2391d", "6bb6737e22bd48d786b02051d077e8cc", "e97a0201b81446b882ba802a5a3b00e8", "c1a89a042b044278a8666da306e6a481", "21ce216193f346c09a04a1d64bc0cd8a", "23ee9c5c18c64305adc55ee218afd7a9", "1f632bdc00e84422b64c0a4b8e238f34", "8d50d82f9d94482c9883f99ea5c7d704", "6a2dea21e7ce4eda8953995497308327", "2f3d1d2f1c92402cadc4cfa1b0094238", "844caba5ebdd48189517a10705543292", "2d667cd4bc134a44958d17ef75d86321", "acfb7b6734884939a753fc19047bb9bc", "73cd51c6c43b40cab25d411e7c0f6ad1", "19c7ea1e9364437a9c0bf6b4e645f854", "8675aad817a44ab39b097ec864669833", "5664e1233a904f4bb4af9c5322517fab", "0ae5110b687440e89ebebfb847985aa1", "85509b0aafec47e5ad540abc7ae4ab7d", "d3d7c15d53c8498e823c84fe609321dd", "80de3739b91a45478cb6a00dcaeae756", "7c99e16722cf4fab866732557769b921", "00834c084ddb4f46ac29f14575a2383d", "4ce1e695a6ab4906ba0817005cf58d55", "eb03476353ef4568b94a3071918e72f2", "1b8307d133bf4280845f7d3a302b3a2b", "daee15869a92459aabcd9128526183d5", "5ddd9e1a1fab4531930acaf08cc45c73", "4081e141986f4abba15477a12a752ca0", "892e150a80464f8198024587a47186ba", "9f9e15ff2e394ee7a7776e3e7f4b7d30", "7e264d54d38e4d02acfd47e4e533b49d", "746302ee5d21495db5d9a13789c5256c", "47f7ff7b213e405a822ad6fe2e5de431", "e4810a798c0f47b6b54f84ff4ffec608", "8e480fe546f24afebb1ea723bff84456", "0cf925f582374cd197164625f0ddf27d", "aa7557fbffc54ed3a9c58c1531fd93f6", "061c90e4148b43b8ba65848ca4c1ba46", "f617d181ffdb4b1e8d81fbb393923a6e", "a26ddb684a07496da4290e3f6031b685", "f20fc82bcbf245619ad4dec04d0f999d", "8016eabfc7aa48c2bc0bf3a13919a675", "83cb83e404a24fba8cf43610cb1696fc", "7b70db203ee644fe81006f5d48aa426d", "efd9d5724dbd435991052b4445c6970f", "41f65340441f419d91e1d3841921f48a", "58aba8edefa44c60818891a2651137be", "e5e8f119a91944f296ff821dd7ecfb1b", "568a85caf800461b8571e3d206854e6b", "e2a8a379bd0d4cbdb22fbc3bbb4fdc7a", "2c7d952f958247b681301d1c6bff4fa7", "27649a0d303643d8985caf506ef66fc3", "1855ae0388074d08a4763b7ac76c6948", "7e5252f608d6468f80deb468cb2556fd", "bce6b62300d942a1ab89a6f0ceb16d30", "0239f7263d1a4e8b9475ae48379c068d", "5b527e45d6a946b28047115fa6b5aa3a", "3c5eb07f0bff43948ff1f283c3c56b0f", "3545fcc1e9d7453099c4931f658e0bc8", "2f314258091a430095794c3fe0aea7e3", "0ebfa123462342e99ad81b7e5ee00eac", "8af6bd6e8cae4964a2e372be30220bd9", "4184f160ab6d4f58bc921fb7ba89cf60", "575c4d8d503244d8a9f2a5709021b5b2", "ffce35af1ad84a2c838cca55a26dd3c4", "d21cd4878c6e49d38dd3abb2e3b3f566", "969e3cf7f3634c3f90b5fea38c5797ca", "78501c2a5ac84f9ca0d20bbef340fc9f", "5b4fbd1102a84670a1eed6f3d25c0bcd", "621880c98245427881dd5b004b480c6a", "65c2716dd7f14afa93d3bfaebe85c44f", "2ff541d18f5844408690be00c7259d59", "679fa9d2a703494dbb10fbfd19879f1a", "6717b182e5674afb90006b50dea98cae", "0887c7aabbbe4d4e8fdc64d2e2657cc7" ] }, "id": "a2787582-554f-44ce-9f38-4180a5ed6b44", "outputId": "b1729004-591e-41c2-c206-9d0e572eb8e5" }, "outputs": [], "source": [ "from datasets import load_dataset, DatasetDict\n", "\n", "common_voice = DatasetDict()\n", "\n", "common_voice[\"train\"] = load_dataset(dataset_name, language_abbr, split=\"train+validation\", use_auth_token=True)\n", "common_voice[\"test\"] = load_dataset(dataset_name, language_abbr, split=\"test\", use_auth_token=True)\n", "\n", "print(common_voice)" ] }, { "cell_type": "code", "execution_count": null, "id": "20ba635d-518c-47ac-97ee-3cad25f1e0ce", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "20ba635d-518c-47ac-97ee-3cad25f1e0ce", "outputId": "dd81bced-f544-4d55-9669-5babb901f842" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "DatasetDict({\n", " train: Dataset({\n", " features: ['audio', 'sentence'],\n", " num_rows: 3927\n", " })\n", " test: Dataset({\n", " features: ['audio', 'sentence'],\n", " num_rows: 1816\n", " })\n", "})\n" ] } ], "source": [ "common_voice = common_voice.remove_columns(\n", " [\"accent\", \"age\", \"client_id\", \"down_votes\", \"gender\", \"locale\", \"path\", \"segment\", \"up_votes\"]\n", ")\n", "\n", "print(common_voice)" ] }, { "cell_type": "markdown", "id": "2d63b2d2-f68a-4d74-b7f1-5127f6d16605", "metadata": { "id": "2d63b2d2-f68a-4d74-b7f1-5127f6d16605" }, "source": [ "## Prepare Feature Extractor, Tokenizer and Data" ] }, { "cell_type": "code", "execution_count": null, "id": "bc77d7bb-f9e2-47f5-b663-30f7a4321ce5", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "4d25d9919acf44a19f1b6f8fd625f808", "b041efb27b6149fcaf206590f1c1b961", "4668602d021a400a8b0ec88599abc85c", "92d9732acb964601b27695041f9fbb72", "c3ea17f7dd94462986d30b751664d77b", "1b1dc31d9a2b4357a71119300c6d899a", "7a24ae9d13fb4e82b1993b05f8d71d11", "e1141861d9f44d4c95313fd432795b70", "6ef55b5db76d4c78854fa0c27165e480", "91b103ac79e641b190416acdeb55902c", "e9e10f1e53b74509bfc9c0bf11502c5e" ] }, "id": "bc77d7bb-f9e2-47f5-b663-30f7a4321ce5", "outputId": "7abb2062-e755-4f1a-e88b-9b7bf2986dbf" }, "outputs": [], "source": [ "from transformers import WhisperFeatureExtractor\n", "\n", "feature_extractor = WhisperFeatureExtractor.from_pretrained(model_name_or_path)" ] }, { "cell_type": "code", "execution_count": null, "id": "c7b07f9b-ae0e-4f89-98f0-0c50d432eab6", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 209, "referenced_widgets": [ "f436e6a7d3014e2ca44c94455bfeace8", "f9d6d41ffeba43cf94ec9d7a96af6617", "bed65245e7234977874d35bf78694e35", "c28c2fc81e0e467cbc0ded0205d1ee85", "ea941a078b984f51b66b5f1e8f3d1d82", "e3f1244dbe2c48bc8102f958c3df6467", "786d3d34cfd24f81996797c55b10b443", "3a4bb5b9cf864265af8f1a0b989dff2c", "ac2e1977ddaf4b948c9cc24d84c08b83", "dd339bf6baf6433e92665e30dc30b062", "6a544d6dd5954beab32ce3089d8d2aac", "f933fbdcc26d41b0bb294dabd0337834", "950e74921e6042868ab6b7b9070d6f69", "82dc91c5b065459d827863699a9710e1", "5b7b6f08765c4c1989f945414f2c3cf4", "735c3606df924b9297ddc05fae3e92d5", "d5d632dd16f147e090c62aad38c45d3c", "24191f38e3654b86a5da3576615e2229", "4b1f4abe697948d5a1cca9b45b2a6f87", "8527f474f1a549abafe9519e2b4bd338", "1031dc4b5d2d45f3b14ab12dbd9bca56", "ab40616ed9b74f438e74d403f848bed4", "e6c2dc814c324a0c8cb744ca16707479", "a615446a48624d0f9a009c1f6d8b1a54", "383d6e891c5249b4b2fabb60d4900488", "3c3214f235a54864848902f7e53662db", "73f6e6860b64491284c9442f0deae8b6", "6d9826a5adb847e8b4cc4486a31b52ce", "e8b3093587e44164b0ac043414cea0fa", "96266c5722f54eeeb682b2707b8025dd", "f0be69583cd1410da6dbc18302d4439b", "c24ddbaafa5f4a63b391d981a4f10354", "629725dfcc684b1db1a57850c3bc7bc3", "add0f175631742d49dd4695bc004e81a", "5871a46d60f14da99e4bb8ee74405319", "64b308019fff4095ab7f1812aab4676a", "03f62b3f5aa64b6390b157cb3f9d2b9f", "59657f26f4af490980b1d9bea1526e5e", "1571fa5d8f494704949c5809f3409fa7", "5ef218872f2646249888def0abed7149", "03fcbc61c50c4ddc9c3684e4c085bffb", "e4234c7c29744fc4be99b8b2ebedc9d1", "70aa1fc14b09473e911dad3f9030b15b", "008bead021ea416eb31dd9143d5ae5b9", "ebd79e22ad4e4256a6d88883af2c1eef", "c47ba0b11e074f708338863a35a78f7b", "72b715be2c774235a21602b18d71e75a", "ff9a0ed54bab49aca6f27bf1be66958e", "15b5e415b62146ba96215458cf116431", "8b0dd001d5b04647b1c480aab83d03a2", "0af935ad4f694fb48094e6a119cbcf82", "7cfbb542eba34459ba64b881c1040eee", "fe48e65b2371445bbb01a8d3e9af1f67", "b8d3539c8a454217a3b6ffab51259054", "05e96819517e417aaf05f5f38c0c8b76", "b600ead93bbf44a3a3fe229589c63a61", "3d4c614fb775434fb0c5b02d46246ee0", "344d1cc53e28411cae839a4ebba1bf58", "a1578c0c777f4780a3fdd1635a0909d9", "41faf55c8878475f8a986b02ad73e8ce", "b5c8221a09df4dfdb74017d4af544b95", "65e4b8dae1e4435cb7737a8d5dd13d91", "6d932eadfd6a448a9a71d741bb64f428", "dce0d285c7d947dfba9ee5bc1a6ebece", "bde010029c374a0eb2bb942f380f0e8b", "a91f37ce798d411ea2a33cfdb1f01251" ] }, "id": "c7b07f9b-ae0e-4f89-98f0-0c50d432eab6", "outputId": "4c094075-3f95-42db-9f4b-4db0c2fbeb91" }, "outputs": [], "source": [ "from transformers import WhisperTokenizer\n", "\n", "tokenizer = WhisperTokenizer.from_pretrained(model_name_or_path, language=language, task=task)" ] }, { "cell_type": "code", "execution_count": null, "id": "77d9f0c5-8607-4642-a8ac-c3ab2e223ea6", "metadata": { "id": "77d9f0c5-8607-4642-a8ac-c3ab2e223ea6" }, "outputs": [], "source": [ "from transformers import WhisperProcessor\n", "\n", "processor = WhisperProcessor.from_pretrained(model_name_or_path, language=language, task=task)" ] }, { "cell_type": "markdown", "id": "381acd09-0b0f-4d04-9eb3-f028ac0e5f2c", "metadata": { "id": "381acd09-0b0f-4d04-9eb3-f028ac0e5f2c" }, "source": [ "### Prepare Data" ] }, { "cell_type": "code", "execution_count": null, "id": "6e6b0ec5-0c94-4e2c-ae24-c791be1b2255", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 72 }, "id": "6e6b0ec5-0c94-4e2c-ae24-c791be1b2255", "outputId": "1f1fe2d1-3ad2-42d4-e6f0-f0929785ae8e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'audio': {'path': '/root/.cache/huggingface/datasets/downloads/extracted/f7e1ef6a2d14f20194999aad5040c5d4bb3ead1377de3e1bbc6e9dba34d18a8a/common_voice_mr_30585613.mp3', 'array': array([-1.3727526e-15, -1.2400461e-13, -1.5159097e-13, ...,\n", " 4.7928120e-06, 3.5631349e-06, 1.6352631e-06], dtype=float32), 'sampling_rate': 48000}, 'sentence': 'рдЖрдИрдЪреЗ рдЖрдЬрд╛рд░рдкрдг рд╡рд╛рдврдд рдЪрд╛рд▓рд▓реЗ, рддрд╕рддрд╢реА рдордереАрд╣реА рдиреАрдЯ рдЦрд╛рддрдкреАрддрдирд╛рд╢реА рдЭрд╛рд▓реА.'}\n" ] } ], "source": [ "print(common_voice[\"train\"][0])" ] }, { "cell_type": "markdown", "id": "5a679f05-063d-41b3-9b58-4fc9c6ccf4fd", "metadata": { "id": "5a679f05-063d-41b3-9b58-4fc9c6ccf4fd" }, "source": [ "Since \n", "our input audio is sampled at 48kHz, we need to _downsample_ it to \n", "16kHz prior to passing it to the Whisper feature extractor, 16kHz being the sampling rate expected by the Whisper model. \n", "\n", "We'll set the audio inputs to the correct sampling rate using dataset's \n", "[`cast_column`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=cast_column#datasets.DatasetDict.cast_column)\n", "method. This operation does not change the audio in-place, \n", "but rather signals to `datasets` to resample audio samples _on the fly_ the \n", "first time that they are loaded:" ] }, { "cell_type": "code", "execution_count": null, "id": "f12e2e57-156f-417b-8cfb-69221cc198e8", "metadata": { "id": "f12e2e57-156f-417b-8cfb-69221cc198e8" }, "outputs": [], "source": [ "from datasets import Audio\n", "\n", "common_voice = common_voice.cast_column(\"audio\", Audio(sampling_rate=16000))" ] }, { "cell_type": "markdown", "id": "00382a3e-abec-4cdd-a54c-d1aaa3ea4707", "metadata": { "id": "00382a3e-abec-4cdd-a54c-d1aaa3ea4707" }, "source": [ "Re-loading the first audio sample in the Common Voice dataset will resample \n", "it to the desired sampling rate:" ] }, { "cell_type": "code", "execution_count": null, "id": "87122d71-289a-466a-afcf-fa354b18946b", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "87122d71-289a-466a-afcf-fa354b18946b", "outputId": "727a709a-2b21-4c54-807f-efd40ea1719c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'audio': {'path': '/root/.cache/huggingface/datasets/downloads/extracted/f7e1ef6a2d14f20194999aad5040c5d4bb3ead1377de3e1bbc6e9dba34d18a8a/common_voice_mr_30585613.mp3', 'array': array([-4.4097186e-14, -9.4153831e-14, 3.4645775e-13, ...,\n", " -7.6018655e-06, -1.8617659e-06, 4.4520480e-06], dtype=float32), 'sampling_rate': 16000}, 'sentence': 'рдЖрдИрдЪреЗ рдЖрдЬрд╛рд░рдкрдг рд╡рд╛рдврдд рдЪрд╛рд▓рд▓реЗ, рддрд╕рддрд╢реА рдордереАрд╣реА рдиреАрдЯ рдЦрд╛рддрдкреАрддрдирд╛рд╢реА рдЭрд╛рд▓реА.'}\n" ] } ], "source": [ "print(common_voice[\"train\"][0])" ] }, { "cell_type": "markdown", "id": "91edc72d-08f8-4f01-899d-74e65ce441fc", "metadata": { "id": "91edc72d-08f8-4f01-899d-74e65ce441fc" }, "source": [ "Now we can write a function to prepare our data ready for the model:\n", "1. We load and resample the audio data by calling `batch[\"audio\"]`. As explained above, ЁЯдЧ Datasets performs any necessary resampling operations on the fly.\n", "2. We use the feature extractor to compute the log-Mel spectrogram input features from our 1-dimensional audio array.\n", "3. We encode the transcriptions to label ids through the use of the tokenizer." ] }, { "cell_type": "code", "execution_count": null, "id": "6525c478-8962-4394-a1c4-103c54cce170", "metadata": { "id": "6525c478-8962-4394-a1c4-103c54cce170" }, "outputs": [], "source": [ "def prepare_dataset(batch):\n", " # load and resample audio data from 48 to 16kHz\n", " audio = batch[\"audio\"]\n", "\n", " # compute log-Mel input features from input audio array\n", " batch[\"input_features\"] = feature_extractor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_features[0]\n", "\n", " # encode target text to label ids\n", " batch[\"labels\"] = tokenizer(batch[\"sentence\"]).input_ids\n", " return batch" ] }, { "cell_type": "markdown", "id": "70b319fb-2439-4ef6-a70d-a47bf41c4a13", "metadata": { "id": "70b319fb-2439-4ef6-a70d-a47bf41c4a13" }, "source": [ "We can apply the data preparation function to all of our training examples using dataset's `.map` method. The argument `num_proc` specifies how many CPU cores to use. Setting `num_proc` > 1 will enable multiprocessing. If the `.map` method hangs with multiprocessing, set `num_proc=1` and process the dataset sequentially." ] }, { "cell_type": "code", "execution_count": null, "id": "7b73ab39-ffaf-4b9e-86e5-782963c6134b", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 197, "referenced_widgets": [ "466eeda389c442e487742faff05eeb81", "664ff40a4e7346869652ba3be663acf2", "3d8f10b5726b46de934dd0f7ed7244e4", "b803893882ac4358a4676964dfb3fb31", "4ff7e3d07f6a48f8935f0f42b86fc20c", "3e5db1000a6c4871936614e712e769db", "21dcf39a421847a78ea685eec0983fc6", "c4119f7ec7464aab90f17d022e674999", "965ac01b68064dc5a9fc3bb3f244c804", "f5d7433d15de45e997d12568cac536fc", "14e284f308844311a9ad40415091d93f", "58c3766293e64116a131357f6cc66fe5", "abc8f69eae7b46b1b430cf3b7a231b05", "979edd227f0840a4be0233082e452b5a", "12dedd9f089c42f3a8bba9adb01cf9d3", "3042d874fcf544fabc4701938bca7cf3", "baa8f739bb7e402bae7ee479e282e813", "54509d703b7d4416bdee59157f918396", "ec6017ce2fb3431ab823bde05f977a61", "1a540a7cba794122abdb1900061479e2", "d046a46c70ea46ffbb04a3c9f55637d5", "99b628071c814d88a3cd5d72e4c95f01", "e213d1c919314315ada180d49e27dfb6", "0ea1f163e4174684bd6efc2e2433c1d3", "09511a81a89d4754897a3507a84405be", "8338339ab8a242c1a485bce8558f9c39", "8e84abf61e3e45d58efc7ccd0bfe8d37", "47a5e0cfb3564c16acaedb88613b9020", "09e357a7187044b0bd2b841893a2601f", "65e8c45d05be4d41b136439d9785a09c", "1442a54fad7d44ddbafdacaf7f95279a", "4bea6b0455ae4019b33d45491dbf4584", "9c928a14371a4d9aa521953e287fff54", "b8acdb71c3564972a6c8b27e964ec061", "688f552e85714fe6a5d3eda82be0106a", "a8aac44a077040bd9f4638c0c8f7a877", "89f8ede64593475da4e056e9907f370f", "851902cb2b0e494998684409d82dafd1", "328f4d11886d48f49d4578e8e352097f", "a25ef41450eb42ff9b8b618b40080ac0", "04144ed3ff5f423f99179702cee5343f", "8c41e1ee1a2c49b8b3d3d320fbf26262", "4edf0948ed1346ceba1c0148e9c6fe1c", "40f2b2ff75ca4562808fc2c7a870901e" ] }, "id": "7b73ab39-ffaf-4b9e-86e5-782963c6134b", "outputId": "eecac4c8-c5f8-427b-a2ba-0d51fae825ac" }, "outputs": [], "source": [ "common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice.column_names[\"train\"], num_proc=2)" ] }, { "cell_type": "code", "execution_count": null, "id": "c4be572c", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "c4be572c", "outputId": "0383124a-d4b1-4abe-a8a1-868ce6b3884e" }, "outputs": [ { "data": { "text/plain": [ "Dataset({\n", " features: ['input_features', 'labels'],\n", " num_rows: 3927\n", "})" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "common_voice[\"train\"]" ] }, { "cell_type": "markdown", "id": "263a5a58-0239-4a25-b0df-c625fc9c5810", "metadata": { "id": "263a5a58-0239-4a25-b0df-c625fc9c5810" }, "source": [ "## Training and Evaluation" ] }, { "cell_type": "markdown", "id": "8d230e6d-624c-400a-bbf5-fa660881df25", "metadata": { "id": "8d230e6d-624c-400a-bbf5-fa660881df25" }, "source": [ "### Define a Data Collator" ] }, { "cell_type": "code", "execution_count": null, "id": "8326221e-ec13-4731-bb4e-51e5fc1486c5", "metadata": { "id": "8326221e-ec13-4731-bb4e-51e5fc1486c5" }, "outputs": [], "source": [ "import torch\n", "\n", "from dataclasses import dataclass\n", "from typing import Any, Dict, List, Union\n", "\n", "\n", "@dataclass\n", "class DataCollatorSpeechSeq2SeqWithPadding:\n", " processor: Any\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 lengths and need different padding methods\n", " # first treat the audio inputs by simply returning torch tensors\n", " input_features = [{\"input_features\": feature[\"input_features\"]} for feature in features]\n", " batch = self.processor.feature_extractor.pad(input_features, return_tensors=\"pt\")\n", "\n", " # get the tokenized label sequences\n", " label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n", " # pad the labels to max length\n", " labels_batch = self.processor.tokenizer.pad(label_features, return_tensors=\"pt\")\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", " # if bos token is appended in previous tokenization step,\n", " # cut bos token here as it's append later anyways\n", " if (labels[:, 0] == self.processor.tokenizer.bos_token_id).all().cpu().item():\n", " labels = labels[:, 1:]\n", "\n", " batch[\"labels\"] = labels\n", "\n", " return batch" ] }, { "cell_type": "markdown", "id": "3cae7dbf-8a50-456e-a3a8-7fd005390f86", "metadata": { "id": "3cae7dbf-8a50-456e-a3a8-7fd005390f86" }, "source": [ "Let's initialise the data collator we've just defined:" ] }, { "cell_type": "code", "execution_count": null, "id": "fc834702-c0d3-4a96-b101-7b87be32bf42", "metadata": { "id": "fc834702-c0d3-4a96-b101-7b87be32bf42" }, "outputs": [], "source": [ "data_collator = DataCollatorSpeechSeq2SeqWithPadding(processor=processor)" ] }, { "cell_type": "markdown", "id": "d62bb2ab-750a-45e7-82e9-61d6f4805698", "metadata": { "id": "d62bb2ab-750a-45e7-82e9-61d6f4805698" }, "source": [ "### Evaluation Metrics" ] }, { "cell_type": "markdown", "id": "66fee1a7-a44c-461e-b047-c3917221572e", "metadata": { "id": "66fee1a7-a44c-461e-b047-c3917221572e" }, "source": [ "We'll use the word error rate (WER) metric, the 'de-facto' metric for assessing \n", "ASR systems. For more information, refer to the WER [docs](https://huggingface.co/metrics/wer). We'll load the WER metric from ЁЯдЧ Evaluate:" ] }, { "cell_type": "code", "execution_count": null, "id": "b22b4011-f31f-4b57-b684-c52332f92890", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "215c3486e13343e091a26d658e1030d2", "999382fb9e764a98893bd5269261d70b", "4e9729771294424f959bbfb6bf8b60f3", "3d2249516ecd43a08b5fba53fddb32e8", "681711ebd0c64a63bee6b5337ef401db", "d37791ea2b6c4152991295ca0edb0fb7", "730f4d93bbd94452a59de43bf3d0a266", "048e5c87cea34014a8f8a4538f4126bd", "fbd27061ff114846aedc99bc2d17f7a7", "5a306409e9e045b2b936267c520f935c", "9f0638198f544a3bbb31a3a78b7bd2c2" ] }, "id": "b22b4011-f31f-4b57-b684-c52332f92890", "outputId": "b0a08086-69b9-4ab4-97ac-dbed295f2e15" }, "outputs": [], "source": [ "import evaluate\n", "\n", "metric = evaluate.load(\"wer\")" ] }, { "cell_type": "markdown", "id": "4f32cab6-31f0-4cb9-af4c-40ba0f5fc508", "metadata": { "id": "4f32cab6-31f0-4cb9-af4c-40ba0f5fc508" }, "source": [ "We then simply have to define a function that takes our model \n", "predictions and returns the WER metric. This function, called\n", "`compute_metrics`, first replaces `-100` with the `pad_token_id`\n", "in the `label_ids` (undoing the step we applied in the \n", "data collator to ignore padded tokens correctly in the loss).\n", "It then decodes the predicted and label ids to strings. Finally,\n", "it computes the WER between the predictions and reference labels:" ] }, { "cell_type": "code", "execution_count": null, "id": "23959a70-22d0-4ffe-9fa1-72b61e75bb52", "metadata": { "id": "23959a70-22d0-4ffe-9fa1-72b61e75bb52" }, "outputs": [], "source": [ "def compute_metrics(pred):\n", " pred_ids = pred.predictions\n", " label_ids = pred.label_ids\n", "\n", " # replace -100 with the pad_token_id\n", " label_ids[label_ids == -100] = tokenizer.pad_token_id\n", "\n", " # we do not want to group tokens when computing the metrics\n", " pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)\n", " label_str = tokenizer.batch_decode(label_ids, skip_special_tokens=True)\n", "\n", " wer = 100 * metric.compute(predictions=pred_str, references=label_str)\n", "\n", " return {\"wer\": wer}" ] }, { "cell_type": "markdown", "id": "daf2a825-6d9f-4a23-b145-c37c0039075b", "metadata": { "id": "daf2a825-6d9f-4a23-b145-c37c0039075b" }, "source": [ "###┬аLoad a Pre-Trained Checkpoint" ] }, { "cell_type": "markdown", "id": "437a97fa-4864-476b-8abc-f28b8166cfa5", "metadata": { "id": "437a97fa-4864-476b-8abc-f28b8166cfa5" }, "source": [ "Now let's load the pre-trained Whisper `small` checkpoint. Again, this \n", "is trivial through use of ЁЯдЧ Transformers!" ] }, { "cell_type": "code", "execution_count": null, "id": "5a10cc4b-07ec-4ebd-ac1d-7c601023594f", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 220, "referenced_widgets": [ "d8c1a66480204f1095ff5f6a7dd2e477", "9dc736113ef6477d91aaf71c9969ca74", "79521628c64b4d6f9f22e73749298693", "c7268972f75e4824893ebe7d893a18e1", "a87598d464174703b5f5a5eca23543f3", "e0529b81739144db8912c2d6789e729a", "13d0a97497274652b081cbcefb3fd17d", "08b26adf061b48f59078bf0c0b59e643", "ef056ad59e314089a012acaa73a54e4f", "7935e298049f4deeaeb278ba3de92291", "3afca90970fc4925a05a9a1aa5c8d2f2", "40aba44ef0a74c0d9a385c803c1365a6", "ae54388d78dc4b7ebd1b21860421ffe4", "bb4a47c63d254d4aa3220e85f86d37c4", "cd585c98560b42c8b4a08df5b853b23c", "a140cd385e5a4c0a88c5562370982d2e", "fce64f5690024c698701330f0e5d039a", "abd0e7c414974e51b280a29bf978f776", "bf7961a79c2f403a89a4fb6d4b1a02e5", "1d8636d1d1c3442fbbc2fc83cd03fa44", "313090dc1f034ab19d5ffc573ea1aa5c", "966c0400dafe4e3ea2c9baebc2e104fa", "9087be5d992c4198b3ec2c61f4021164", "6bc07db3471342a6bbc1b86ca734b3e6", "4f167e9657274d56b8568d48262d1ee6", "b7fbaa9d4bcd40b5bcf8d1475658c5b1", "ba21f5ddf2434cc792b70a70c8c1079e", "21bc84c704874455a57c47239984d28d", "5b226e580df94448bed54b400c7a9a25", "b0237154051343adaa076a9cc6dd711d", "64756452791144859b9c803886c6dc77", "f027b4358c2c41a8a93c617641135bcd", "42cb6114956c4a86980c8dafd5a734ae" ] }, "id": "5a10cc4b-07ec-4ebd-ac1d-7c601023594f", "outputId": "163d4b39-7e5d-4126-8d78-846c5d94dca6" }, "outputs": [], "source": [ "from transformers import WhisperForConditionalGeneration\n", "\n", "model = WhisperForConditionalGeneration.from_pretrained(model_name_or_path, load_in_8bit=True)\n", "\n", "# model.hf_device_map - this should be {\" \": 0}" ] }, { "cell_type": "markdown", "id": "a15ead5f-2277-4a39-937b-585c2497b2df", "metadata": { "id": "a15ead5f-2277-4a39-937b-585c2497b2df" }, "source": [ "Override generation arguments - no tokens are forced as decoder outputs (see [`forced_decoder_ids`](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate.forced_decoder_ids)), no tokens are suppressed during generation (see [`suppress_tokens`](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate.suppress_tokens)):" ] }, { "cell_type": "code", "execution_count": null, "id": "62038ba3-88ed-4fce-84db-338f50dcd04f", "metadata": { "id": "62038ba3-88ed-4fce-84db-338f50dcd04f" }, "outputs": [], "source": [ "model.config.forced_decoder_ids = None\n", "model.config.suppress_tokens = []" ] }, { "attachments": {}, "cell_type": "markdown", "id": "bR-_yaEOPsfQ", "metadata": { "id": "bR-_yaEOPsfQ" }, "source": [ "### Post-processing on the model\n", "\n", "Finally, we need to apply some post-processing on the 8-bit model to enable training, let's freeze all our layers, and cast all non `int8` layers in `float32` for stability." ] }, { "cell_type": "code", "execution_count": null, "id": "Cl_ZQualPt9R", "metadata": { "id": "Cl_ZQualPt9R" }, "outputs": [], "source": [ "from peft import prepare_model_for_int8_training\n", "\n", "model = prepare_model_for_int8_training(model)" ] }, { "cell_type": "markdown", "id": "Vjl4j4RJPmPR", "metadata": { "id": "Vjl4j4RJPmPR" }, "source": [ "### Apply LoRA\n", "\n", "Here comes the magic with `peft`! Let's load a `PeftModel` and specify that we are going to use low-rank adapters (LoRA) using `get_peft_model` utility function from `peft`." ] }, { "cell_type": "code", "execution_count": null, "id": "DQtpDPRHPyOL", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "DQtpDPRHPyOL", "outputId": "1effcbde-7acc-4f62-f24b-e6236a43f833" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "trainable params: 15728640 || all params: 1559033600 || trainable%: 1.0088711365810203\n" ] } ], "source": [ "from peft import LoraConfig, PeftModel, LoraModel, LoraConfig, get_peft_model\n", "\n", "config = LoraConfig(r=32, lora_alpha=64, target_modules=[\"q_proj\", \"v_proj\"], lora_dropout=0.05, bias=\"none\")\n", "\n", "model = get_peft_model(model, config)\n", "model.print_trainable_parameters()" ] }, { "cell_type": "markdown", "id": "3906d436", "metadata": {}, "source": [ "We are ONLY using **1%** of the total trainable parameters, thereby performing **Parameter-Efficient Fine-Tuning**" ] }, { "cell_type": "markdown", "id": "2178dea4-80ca-47b6-b6ea-ba1915c90c06", "metadata": { "id": "2178dea4-80ca-47b6-b6ea-ba1915c90c06" }, "source": [ "### Define the Training Configuration" ] }, { "cell_type": "markdown", "id": "c21af1e9-0188-4134-ac82-defc7bdcc436", "metadata": { "id": "c21af1e9-0188-4134-ac82-defc7bdcc436" }, "source": [ "In the final step, we define all the parameters related to training. For more detail on the training arguments, refer to the Seq2SeqTrainingArguments [docs](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.Seq2SeqTrainingArguments)." ] }, { "cell_type": "code", "execution_count": null, "id": "0ae3e9af-97b7-4aa0-ae85-20b23b5bcb3a", "metadata": { "id": "0ae3e9af-97b7-4aa0-ae85-20b23b5bcb3a" }, "outputs": [], "source": [ "from transformers import Seq2SeqTrainingArguments\n", "\n", "training_args = Seq2SeqTrainingArguments(\n", " output_dir=\"temp\", # change to a repo name of your choice\n", " per_device_train_batch_size=8,\n", " gradient_accumulation_steps=1, # increase by 2x for every 2x decrease in batch size\n", " learning_rate=1e-3,\n", " warmup_steps=50,\n", " num_train_epochs=3,\n", " evaluation_strategy=\"epoch\",\n", " fp16=True,\n", " per_device_eval_batch_size=8,\n", " generation_max_length=128,\n", " logging_steps=25,\n", " remove_unused_columns=False, # required as the PeftModel forward doesn't have the signature of the wrapped model's forward\n", " label_names=[\"labels\"], # same reason as above\n", ")" ] }, { "cell_type": "markdown", "id": "b3a944d8-3112-4552-82a0-be25988b3857", "metadata": { "id": "b3a944d8-3112-4552-82a0-be25988b3857" }, "source": [ "**Few Important Notes:**\n", "1. `remove_unused_columns=False` and `label_names=[\"labels\"]` are required as the PeftModel's forward doesn't have the signature of the base model's forward.\n", "\n", "2. INT8 training required autocasting. `predict_with_generate` can't be passed to Trainer because it internally calls transformer's `generate` without autocasting leading to errors. \n", "\n", "3. Because of point 2, `compute_metrics` shouldn't be passed to `Seq2SeqTrainer` as seen below. (commented out)" ] }, { "cell_type": "code", "execution_count": null, "id": "d546d7fe-0543-479a-b708-2ebabec19493", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "d546d7fe-0543-479a-b708-2ebabec19493", "outputId": "e2fabe64-2c50-42ff-a7ca-7773813e9408" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "The model is loaded in 8-bit precision. To train this model you need to add additional modules inside the model such as adapters using `peft` library and freeze the model weights. Please check the examples in https://github.com/huggingface/peft for more details.\n", "Using cuda_amp half precision backend\n" ] } ], "source": [ "from transformers import Seq2SeqTrainer, TrainerCallback, TrainingArguments, TrainerState, TrainerControl\n", "from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR\n", "\n", "\n", "class SavePeftModelCallback(TrainerCallback):\n", " def on_save(\n", " self,\n", " args: TrainingArguments,\n", " state: TrainerState,\n", " control: TrainerControl,\n", " **kwargs,\n", " ):\n", " checkpoint_folder = os.path.join(args.output_dir, f\"{PREFIX_CHECKPOINT_DIR}-{state.global_step}\")\n", "\n", " peft_model_path = os.path.join(checkpoint_folder, \"adapter_model\")\n", " kwargs[\"model\"].save_pretrained(peft_model_path)\n", "\n", " pytorch_model_path = os.path.join(checkpoint_folder, \"pytorch_model.bin\")\n", " if os.path.exists(pytorch_model_path):\n", " os.remove(pytorch_model_path)\n", " return control\n", "\n", "\n", "trainer = Seq2SeqTrainer(\n", " args=training_args,\n", " model=model,\n", " train_dataset=common_voice[\"train\"],\n", " eval_dataset=common_voice[\"test\"],\n", " data_collator=data_collator,\n", " # compute_metrics=compute_metrics,\n", " tokenizer=processor.feature_extractor,\n", " callbacks=[SavePeftModelCallback],\n", ")\n", "model.config.use_cache = False # silence the warnings. Please re-enable for inference!" ] }, { "cell_type": "code", "execution_count": null, "id": "ee8b7b8e-1c9a-4d77-9137-1778a629e6de", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "ee8b7b8e-1c9a-4d77-9137-1778a629e6de", "outputId": "cdea5268-f33a-4d48-ea4a-a9c71576f81d" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.8/dist-packages/transformers/optimization.py:346: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n", " warnings.warn(\n", "***** Running training *****\n", " Num examples = 3927\n", " Num Epochs = 3\n", " Instantaneous batch size per device = 8\n", " Total train batch size (w. parallel, distributed & accumulation) = 8\n", " Gradient Accumulation steps = 1\n", " Total optimization steps = 1473\n", " Number of trainable parameters = 15728640\n", "/usr/local/lib/python3.8/dist-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n", " warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n", "/usr/local/lib/python3.8/dist-packages/bitsandbytes/autograd/_functions.py:298: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n", " warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n" ] }, { "data": { "text/html": [ "\n", "
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" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "\n", "Training completed. Do not forget to share your model on huggingface.co/models =)\n", "\n", "\n" ] }, { "data": { "text/plain": [ "TrainOutput(global_step=1473, training_loss=0.20080567288382556, metrics={'train_runtime': 13136.6638, 'train_samples_per_second': 0.897, 'train_steps_per_second': 0.112, 'total_flos': 2.52799085113344e+19, 'train_loss': 0.20080567288382556, 'epoch': 3.0})" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trainer.train()" ] }, { "cell_type": "code", "execution_count": null, "id": "0576aa2a", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 116, "referenced_widgets": [ "f309d7a096df4f119e6e6871b56913f1", "5f283548f34848af90affe55a169b5a9", "51a4d85c08d745bda70ba0db731dca68", "eb46a602b3ef484daddbcb847957c3e5", "8e2e49c6046e4dc0a0a810b4e58f80cc", "f3f191968f724e9bbb710b86d3657a66", "d97f77f45e1e400494c2fbf2cd9d69a3", "10e52425e59d4c2c8d2a1239b24e5a95", "9ca0667998cb443d9df29fdd09cf90ff", "f22aa0a93d8b44d0a4c412343ec1f48b", "6e6a59f8e7454c2d886634eade47a21f", "92ae14b97e814d51b016e2a14f227a15", "fb8aca596a1c4ac4a7428248b6c6f8b1", "31de6bfcde21400ab02af4fb31d409da", "3c45a19fdf664b92a27be16abf537a03", "9827e382bf3b49b092969d5656dcad7a", "168e19229aa5404bb151e4547bb31283", "d140f5373bf144d1ae5d282e1a65647e", "fa137c931d2c4e579c27893ca8ee1848", "93647f2d98a74109bfc5ca7a9cc23eed", "a478b23c34654615aad4202b8c7089f6", "4553ba4c0ed645dbb9ddfcc88975ab5f" ] }, "id": "0576aa2a", "outputId": "6d3fe4aa-f42a-4428-e070-2272f3b87f5c" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Uploading the following files to smangrul/openai-whisper-large-v2-LORA-colab: adapter_model.bin,adapter_config.json\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f309d7a096df4f119e6e6871b56913f1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Upload 1 LFS files: 0%| | 0/1 [00:00
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