diff --git "a/peft_bnb_whisper_large_v2_training.ipynb" "b/peft_bnb_whisper_large_v2_training.ipynb" new file mode 100644--- /dev/null +++ "b/peft_bnb_whisper_large_v2_training.ipynb" @@ -0,0 +1,20608 @@ +{ + "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", + 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"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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EpochTraining LossValidation Loss
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EpochTraining LossValidation Loss
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