{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "g4JTHJ-IATho" }, "source": [ "# Audio Classification on IPUs - Fine-tuning" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "In this notebook, we will see how to fine-tune one of the [🤗 Transformers](https://github.com/huggingface/transformers) acoustic models to a keyword spotting task. Specifically, we will fine-tune a multi-lingual pre-trained speech model for automatic speech recognition. Keyword spotting detects preregistered keywords by classifying audio clips into a predefined set of words. SUPERB uses the widely used Speech Commands dataset v1.0 for the task. The dataset consists of ten classes of keywords, a class for silence, and an unknown class to include the false positive." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "This notebook is built to run on the **keyword spotting** subset of the [SUPERB dataset](https://huggingface.co/datasets/superb) with any speech model checkpoint from the [🤗 Models Hub](https://huggingface.co/models?pipeline_tag=automatic-speech-recognition&sort=downloads) as long as that model has a version with a sequence classification head (for example [HubertForSequenceClassification](https://huggingface.co/docs/transformers/main/en/model_doc/hubert#transformers.HubertForSequenceClassification)). The model must also be supported by Optimum Graphcore. The IPU config files of the supported models are available in Graphcore's [Hugging Face account](https://huggingface.co/Graphcore). You can also create your own IPU config file locally. It might just need some small adjustments if you decide to use a different dataset than the one used here." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"drawing\"" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "[![Join our Slack Community](https://img.shields.io/badge/Slack-Join%20Graphcore's%20Community-blue?style=flat-square&logo=slack)](https://www.graphcore.ai/join-community)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Environment setup\n", "\n", "The best way to run this demo is on Paperspace Gradient's cloud IPUs because everything is already set up for you.\n", "\n", "To run the demo using other IPU hardware, you need to have the Poplar SDK enabled. Refer to the [Getting Started guide](https://docs.graphcore.ai/en/latest/getting-started.html#getting-started) for your system for details on how to enable the Poplar SDK. Also refer to the [Jupyter Quick Start guide](https://docs.graphcore.ai/projects/jupyter-notebook-quick-start/en/latest/index.html) for how to set up Jupyter to be able to run this notebook on a remote IPU machine." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Dependencies and configuration" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Install the dependencies for this notebook." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!apt update\n", "!apt-get install libsndfile1 -y" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "L1532RVbJgQV" }, "outputs": [], "source": [ "!pip install \"optimum-graphcore>=0.5, <0.6\"\n", "!pip install librosa" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "pdcMxVGEA9Cd" }, "source": [ "We define the model checkpoint, the name for the IPU config and the parameters for batch sizes and gradient accumulation." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "5WMEawzyCEyG" }, "outputs": [], "source": [ "model_checkpoint = \"facebook/hubert-base-ls960\"\n", "ipu_config_name = \"Graphcore/hubert-base-ipu\"\n", "per_device_train_batch_size=2\n", "per_device_eval_batch_size=8\n", "gradient_accumulation_steps=16" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Values for machine size and cache directories can be configured through environment variables or directly in the notebook:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", "n_ipu = int(os.getenv(\"NUM_AVAILABLE_IPU\", 4))\n", "executable_cache_dir = os.getenv(\"POPLAR_EXECUTABLE_CACHE_DIR\", \"/tmp/exe_cache/\") + \"/audio_classification\"" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Sharing your model with the community\n", "\n", "You can share your model with the 🤗 community. You do this by completing the following steps:\n", "\n", "1. Store your authentication token from the 🤗 website. [Sign up to 🤗](https://huggingface.co/join) if you haven't already.\n", "2. Execute the following cell and input your username and password." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Bkpk_JPlCww8" }, "outputs": [], "source": [ "from huggingface_hub import notebook_login\n", "\n", "notebook_login()" ] }, { "cell_type": "markdown", "metadata": { "id": "JJ0986jTDZRC" }, "source": [ "\n", "Then you need to install Git-LFS to upload your model checkpoints:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "t7JEhLjDDcVd" }, "outputs": [], "source": [ "!apt install git-lfs" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "mcE455KaG687" }, "source": [ "## Loading the dataset" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "RD_G2KJgG_bU" }, "source": [ "We will use the [🤗 Datasets](https://github.com/huggingface/datasets) library to download the data and get the accuracy metric we will use for the evaluation. This can be easily done with the functions `load_dataset` and `load_metric`. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "fhVQU42BLd3p" }, "outputs": [], "source": [ "from datasets import load_dataset, load_metric" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 307, "referenced_widgets": [ "4b35a346a5624e0d805105529763b59f", "bcae9955a2e64b48b2d5a52dc763e663", "4980e8a482304b528ba3f0d856a9ba61", "af973d2df1644789b13382aca9de6846", "760a9599c1134b4c9deb1091f027459a", "32f82a774d5041ea94f7f2377417113f", "2ae4fa4aef6e4c188cafa1783ea740c9", "f2130f6a11ef4574aedc867c95b2351a", "3b3e1dc3d7db412fbcdd735184cfd3b5", "65582294bd254c85bbc0289bc7f84223", "c1468f18740c4f17aaf61f7724f01647", "a0a4c103e9dd46fbb0cfbb716cc2bdc3", "33b1472a1d5d403db0e12874d304922b", "7725da6ecfe44f5c81637b7a536c6055", "6ef358421b564a79bfe866913a63d40e", "df5d12e76cab427788d5dc8f671652ff", "826feaefd72340f1b2b93ac2fe4b938f", "d63cf90bde674ac9a73cd469a06ef518", "a3aeb5bd203d4e0e94f55a086f807fca", "99740f273ebe4a988d0693f831b81583", "7ff6e7f5a93541c3a2bd32d64cad093b", "95a9c52cc5a547229d294ada8257f334", "1633b0a206084a0f831b833d510d3f99", "d66b140df94d4b78bb4e5956a1fcd1fb", "ae0387982e7c437faf5e0bd2f4994294", "316d216c29fb4071aeea9e6e16e9fbe3", "9c848e48bd424c6db2d15868b42b66aa", "bfeaf0079e1247d381fddbc01f5e78ea", "b50d88bc92e543379f7b7ff8723a95ca", "0b3cc61d55e3414aa0d9564493614169", "4eab4dcf13d9476c8d0dbccb626ec394", "c41367250ece4edd88fc48964b8bf68f", "52355b153bc9440182a3e8838bf8dbd6", "1e53f3247b6141c189bdb6e69284eafc", "8314b199279e4195ad04bef41faae6b1", "037890b77e174f2a861d4a591548329f", "5e419c1da23b451f965b15014abaa013", "35a72b9d15cc40418a38dd6ca3fae981", "9b6ad2a78d4c4b8b9a7c6a2864aabb8d", "31af9fabf446443a97294a0f07aa5ae9", "cbd44b4b072f4dca852948b21d285668", "78a03ea308bb49be9fc70682c98963d5", "796bc1840caa44348a6b9fa2853f2682", "ffac5a8a04d94fc884b92a98471136fc", "7497304b35e74136bf8b310cb74c7dd0", "a3eb7454787048e0aaeadf3669cb386c", "de4fff5f40144448a196144299241741", "9d69da5675ac401ba909be2e359a029d", "09fcf6ce3cdf46edb7ecedeb8c7e7caa", "89475fc2fe5f4b02b2a47685f72ba6ce", "2b40ff6b1fe844288f518b1792113f56", "1642a40eae404f5fa9a57ae4b825b1e2", "5b52df215a704857b036f4c99ce68301", "5432d1a83d9e4592a366060f01638d22", "6680b3552a314531abd120602224d8d9", "8bb4a780705b4af38ea6a78d8c6eed8a", "dbef5462cdd54550976501749df56ce4", "c3a5b430340549708e9052d2c58ad0d8", "bfb1ea04f1f742e08df2c7cfc20cbe34", "ed886d76ed554e24becc7e258bd8dae8", "f43832f37b814ddba3d87baedf048658", "d6bbd4237c8844d8a95e3a5e205ffcce", "c000aa8c449946d19a011ee25d53a2e4", "b1d589c4e19749c980460bc384cfc8e4", "f76fe18c512048d78791c809c1444333", "ea47bf43425c4ff3be54e5112a0bbc4f", "be41cb2898184c35b7d757daf1438e8c", "0a3365714a684b4ca2124ee050d25a85", "4d4e48011ded4bbfbcfa2e6b8955e5c1", "8c71e09b1e07426cae1d36f105fc0735", "e04ce9ae9af845e5bf3ecc52decc2601", "f90e9363602f49f4aa24fd1cfbd6818e", "e31dc4760583419ab245b641e7730eca", "581b038e507947e5a556e0621968863d", "969528e0f59c43f2873b4a96aff3fdbb", "731c4ae1f86f46a5976461e65e411224", "58012ecb00dd4ee792f9d2fc9a796240", "6ca5faf5fc29476e99c5dc99cbd3d939", "52be20cde10e4b179b49c7c29f15a5eb", "02e5f5185d7e4d8789485b0128a15063", "3069a441c3564077b8a8737600249528", "a4b45e0b82764fdf8e6770349f33e1fc", "4a040c4345664dbf9dd2fb83d1416af5", "5b468422466249f0a20bde74169714ac", "e2b70589aec44985828335fce33ebc03", "91171c943ca74935bf3934443b47c65c", "c2171278e3d94866903f9a68b916d730", "f71fe360e83b46e9996c4f0d127cab45", "3a49119dc3c54e9ca2aacb270b3ea94f", "14cd90e5aad94e21ae7cf0d2c1d7d487", "b5daab135438422ab5fb812b1cc814d7", "d56f29d2d9f54e05924b300f2fd377b3", "c82cb43632a246379950b95853d1e29d", "3b72cf848ffd435d9d011b1730281e2f", "4a533d1b6291469883e0b660a924a637", "fc79b9ed95694b7e864ce7a5f11d6b8a", "eab28c8a670e4e599b92ad786583ccf3", "5e940fcc169f4441bfaab0b9947911d7", "9141e322ae1745baa24c2b6d72157c55", "c138fc2a063c40a8bee34eb2925acc0a", "ed38f5e61c55418488a3a1124d43d270", "711a63d4239e4ddcb2a4a3f1ca4d7198", "e3a97dd51ac34a4f9ff9e283dfab4e27", "3fc57eedf9bc4886bfb8f7dcc6a6a006", "2a6f447a5a264366a05fea2b9a666738", "79d857690939446285d2495d9d58be0e", "e78f82dd76cb414bba582725bb3c493b", "2400ae42fd4147eebc16fc899b28e95d", "87ca03251bc249d8a10211f14fd90e86", "f01fa18cf33e435fb22e34f2e58b3c8c", "5b61e0b2d3174c2faeb828cbfa572d26", "2dd3fa88e95c4cbd97466cdaea8bbd80", "3689b35607384405a47359a1022dd347", "078b3cc06fa64a99be9248605796bf3c", "a8793011693c4dc2a4098d1366bb2488", "5c1f7ad9bf9a4f9cba9070b61e0668b7", "51c2c20949394c36abc99433a51fd88e", "6bf2d413c07c458ba577f3b73d03253d", "8be63a76988b49788a0b72c27fd62f41", "54fc7b9a53b04c0eb8054f5ab45290b6", "2ea27880f8274282b0df8dc254f0b748" ] }, "id": "8UGse36eLeeb", "outputId": "4c3a0a8d-1afe-4655-e22f-c579407685e6" }, "outputs": [], "source": [ "dataset = load_dataset(\"superb\", \"ks\")\n", "metric = load_metric(\"accuracy\")" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "r8mTmFdlHOmN" }, "source": [ "The `dataset` object is a [`DatasetDict`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasetdict), which contains one key for the training, validation and test sets." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "7tjOWPQYLq4u", "outputId": "3bc4c860-b807-48c9-a2f6-7d8a68f0ca70" }, "outputs": [], "source": [ "dataset" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "nfPPNjthI3u2" }, "source": [ "To access an actual element, you need to select a split (\"train\" in the example below), and specify an index:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "BujWoSgyMQlw", "outputId": "bb0e7e31-e0a1-4d3b-b28d-b50cb48d19d2" }, "outputs": [], "source": [ "dataset[\"test\"][1000]" ] }, { "cell_type": "markdown", "metadata": { "id": "f8GpxwfnJCkF" }, "source": [ "As you can see, the `label` field is not an actual string label. By default the `ClassLabel` fields are encoded into integers for convenience:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "n33LZz_ZMU3o", "outputId": "e2526603-c8a6-4103-8d29-95ef93f396f0" }, "outputs": [], "source": [ "dataset[\"train\"].features[\"label\"]" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "5LdpDtScLgeD" }, "source": [ "Let's create an `id2label` dictionary to decode them back to strings to see what they are. The inverse `label2id` dictionary will also be useful for when we load the model later." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 35 }, "id": "UuyXDtQqNUZW", "outputId": "76fc874f-d1a1-474a-aafd-e9aa2f514098" }, "outputs": [], "source": [ "labels = dataset[\"train\"].features[\"label\"].names\n", "label2id, id2label = dict(), dict()\n", "for i, label in enumerate(labels):\n", " label2id[label] = str(i)\n", " id2label[str(i)] = label\n", "\n", "id2label[\"9\"]" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "GD7wBXeIltx0" }, "source": [ "`HuBERT` expects the input to be formatted as a 1-dimensional array of 16 kHz samples. This means that the audio file has to be loaded and resampled.\n", "\n", "Fortunately, `datasets` does this automatically when specifying the `audio` column. For more information on this feature, refer to its [API documentation](https://huggingface.co/docs/datasets/v2.11.0/en/package_reference/main_classes#datasets.Audio).\n", "\n", "Let try it out:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "UKIaV1ndl0j6", "outputId": "ce504d58-05b7-4106-edc6-7c9c76f5e650" }, "outputs": [], "source": [ "dataset[\"test\"][1000][\"audio\"]" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "eEfxyaatlzU-" }, "source": [ "We can see that the audio file has automatically been loaded and that the sampling rate is set to 16kHz which is what `HuBERT` expects as an input." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "TSvqeGihJvp4" }, "source": [ "To get a sense of what the audio clips sound like, the following code will render \n", "some audio samples picked randomly from the dataset. \n", "\n", "**Note**: *You can run the following cell a few times to listen to different audio samples.*" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 562 }, "id": "RI0kb4woNvuU", "outputId": "2ebe6acf-4a98-4d89-ebbf-b6546ff2e516" }, "outputs": [], "source": [ "import random\n", "from IPython.display import Audio, display\n", "\n", "for _ in range(5):\n", " rand_idx = random.randint(0, len(dataset[\"train\"])-1)\n", " example = dataset[\"train\"][rand_idx]\n", " audio = example[\"audio\"]\n", "\n", " print(f'Label: {id2label[str(example[\"label\"])]}')\n", " print(f'Shape: {audio[\"array\"].shape}, sampling rate: {audio[\"sampling_rate\"]}')\n", " display(Audio(audio[\"array\"], rate=audio[\"sampling_rate\"]))\n", " print()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "37K_0FAQoNRM" }, "source": [ "If you run the cell a few times, you'll see that despite slight variations in length, most of the samples are about 1 second long (`duration = audio_length / sampling_rate`). So we can safely truncate and pad the samples to `16000`." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "4zxoikSOjs0K" }, "source": [ "## Preprocessing the data" ] }, { "cell_type": "markdown", "metadata": { "id": "WTupOU88p1lK" }, "source": [ "Before we can feed those audio clips to our model, we need to preprocess them. This is done by a 🤗 Transformers `FeatureExtractor` which will normalize the inputs and put them in a format the model expects, as well as generate the other inputs that the model requires.\n", "\n", "To do all of this, we instantiate our feature extractor with the `AutoFeatureExtractor.from_pretrained` method, which will ensure that we get a preprocessor that corresponds to the model architecture we want to use." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 288, "referenced_widgets": [ "2fafd1b4c6c5483f9922bef1395dba65", "e6548e0782514f58829c032e751d96be", "ddf5c88d13264d468753cf722df5159b", "a7ce0b3ccc5d49b0871b6deb30fb4e03", "d211c0a9b6fd4706a3cd4259070337d4", "0c5d6edfe85e4b6bbef0fd2e4a4a9da0", "716a18a372244e0d9d08651193066dd5", "883471bd025f4d328b6e493cbc129529", "aae1cd3fa0ed40d1bfa50636f0f1cb8c", "3fc2d757a91a4d0fb57d120f83217670", "43bb225a077d48288c757e76a6136a63", "5ff829f89e50495fbbfd95231a2af8cc", "93bee8175cda498ea7999afcc32317ad", "09b0f2ad6c8643e89cb3426bd18388a3", "7b1dfee284974a2d903f807a4cda3392", "648934cfd4354e5aa8bf4477e798165c", "867148449c6c48569575b9e3700d13f3", "2e80578397b54187922758c96a3e16f1", "612fa8cdff6345a7ba957584876df766", "711b319615a143aca0d4735aae65aa57", "466cd25125d94b8a893f0412a85da96b", "338f19b23cce419f884577e0bdbaacb8" ] }, "id": "G1bX4lGAO_d9", "outputId": "ddaa2ff9-c185-4f0b-8897-66fe01eaaa0c" }, "outputs": [], "source": [ "from transformers import AutoFeatureExtractor\n", "\n", "feature_extractor = AutoFeatureExtractor.from_pretrained(model_checkpoint)\n", "feature_extractor" ] }, { "cell_type": "markdown", "metadata": { "id": "-zYhYOuDL9XP" }, "source": [ "As we've noticed earlier, the samples are roughly 1 second long, so let's set it here:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "wX35xSLx-ghC" }, "outputs": [], "source": [ "max_duration = 1.0 # seconds" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "qUtxmoMvqml1" }, "source": [ "We can then write the function that will preprocess our samples. We just feed them to `feature_extractor` with the argument `truncation=True`, as well as the maximum sample length. This will ensure that very long inputs like the ones in the `_silence_` class can be safely batched." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "4O_p3WrpRyej" }, "outputs": [], "source": [ "import numpy as np\n", "def preprocess_function(examples):\n", " audio_arrays = [x[\"array\"] for x in examples[\"audio\"]]\n", " inputs = feature_extractor(\n", " audio_arrays, \n", " sampling_rate=feature_extractor.sampling_rate, \n", " max_length=int(feature_extractor.sampling_rate * max_duration), \n", " truncation=True,\n", " padding=\"max_length\"\n", " )\n", " inputs[\"input_values\"] = [i.astype(np.float16) for i in inputs[\"input_values\"]]\n", " return inputs" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "OlbYCcVZ4hHu" }, "source": [ "The feature extractor will return a list of NumPy arrays for each example:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "qGUvcAlAqKy-", "outputId": "73052908-db3e-48e7-f7a9-cac4ae016535" }, "outputs": [], "source": [ "preprocess_function(dataset['train'][:5])" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "a1Nw4FwN5wCS" }, "source": [ "To apply this function on all clips in our dataset, we just use the `map` method of the `dataset` object. This will apply the function to all the elements of all the splits in `dataset`. This means our training, validation and testing data will be preprocessed with one command." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 405, "referenced_widgets": [ "7e4fb53a3bd24f7f82ca87cdd578419d", "7bf541a338b54a61bf29933ab361c315", "1ce93ae0105540ccbbc65edc62dfacb4", "a9ad24f133d147d082ea356331dc85a2", "32c1022fdf9c41a8b5ca82f84dd79a6e", "6bec4cc7d74343419615172854132ee4", "963ff74cca7a41279be689675195524c", "a0d5f199888b47ab864e2bd9f5476191", "3e9f2f257e954801bae9b5fd4b399146", "ff67c79ff52e480baa64510195818dea", "d743ae8ac06a41668cdda8e66e8db4a4", "e423a8f9cbe94a2db9be2315ec331ed6", "b971ff42d53f4e8f8a9ff54df290dba4", "45bb211acb2d4e968bd0fcc6fcfa68cc", "47a65edad06e4103a01c7c824fdc0128", "26e65879797148319bec3d75f6c1c0ea", "6df3284c58ba4e56ac6665f9dd04bfec", "8a8afbbd699c43b1a1d1b0c9a73e30da", "5d318e9c5eae4dee9e13eb0300dc06c6", "9b386647f3924128ac163737a7ca40c7", "55d11761df614dc0a0af3a22e805f2fd", "33750f20475e43b0ad402d07b256f7fe", "e564387d8dcd469b8dea5014ce62fe67", "18a2f7b99c7e4423b2e90d0dcdb04192", "d31b4ac27f5b4baea7f8414b1c709b80", "905d95d206e6414293269d50ac52c699", "f4a16fbb6ff648b2ac65f01c439c8469", "f3ee953b9aa34f449b1508f72effd0b9", "fee49bab248448c1b3e2cf70d707184a", "040d8af1869c42f492fb11b69835f020", "e7f4a64e4abb4b94bed9d18df72d04df", "13d1759849494181b82925ea9dc95356", "cbee1e19283e48d39b90fcc4a498aa30" ] }, "id": "FfxFgJ_6qPPy", "outputId": "aedec9cc-a9c1-403e-f052-90efea5fb14c" }, "outputs": [], "source": [ "encoded_dataset = dataset.map(preprocess_function, remove_columns=[\"audio\", \"file\"], batched=True, num_proc=64)\n", "encoded_dataset" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "c0OwX3Es580y" }, "source": [ "Even better, the results are automatically cached by the 🤗 Datasets library to avoid spending time on this step the next time you run your notebook. The 🤗 Datasets library is normally smart enough to detect when the function you pass to `map` has changed (and thus shouldn't use the cached data). 🤗 Datasets warns you when it uses cached files. You can pass `load_from_cache_file=False` in the call to `map` to not use the cached files and force the preprocessing to be applied again." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "HOXmyPQ76Qv9" }, "source": [ "## Training the model" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "0a-2YT7O6ayC" }, "source": [ "Now that our data is ready, we can download the pre-trained model and fine-tune it. For classification we use the `AutoModelForAudioClassification` class. Like with the feature extractor, the `from_pretrained` method will download and cache the model for us. As the label IDs and the number of labels are dataset dependent, we pass `num_labels`, `label2id`, and `id2label` alongside `model_checkpoint` here:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 188, "referenced_widgets": [ "facdfa8c9711409dbc854ba83c77f81f", "a7fa4d0f51394388886d867c4353544b", "1f33066c8136472986ab2edb836a8d0a", "750e60cb4444418aabed9459878eab9b", "70c3bd95dd014db09c101f1e0d02fd94", "eb7397f36f2e4a74b91e0d8eb88822be", "033f0a2fd78142729c5d52c9a43fa9a3", "d44649ee285e4675b431ae6585423724", "c229fc03e52a4ddcb22bdedee8c9b663", "f6547339d953418a995c494c13e07321", "d0fc1a8e569346cfaca7343b3a0a27d6" ] }, "id": "X9DDujL0q1ac", "outputId": "a3575a2d-1c4f-4f28-b3d7-6d59812f045f" }, "outputs": [], "source": [ "from transformers import AutoModelForAudioClassification\n", "from optimum.graphcore import IPUConfig, IPUTrainingArguments, IPUTrainer\n", "\n", "num_labels = len(id2label)\n", "model = AutoModelForAudioClassification.from_pretrained(\n", " model_checkpoint, \n", " num_labels=num_labels,\n", " label2id=label2id,\n", " id2label=id2label,\n", " mask_time_prob=0.0,\n", ")\n" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "U8EmET_f6458" }, "source": [ "The warning tells us we are throwing away some weights (the `quantizer` and `project_q` layers) and randomly initializing some others (the `projector` and `classifier` layers). This is expected in this case, because we are removing the head used to pre-train the model on an unsupervised vector quantization objective and replacing it with a new head for which we don't have pre-trained weights, so the library warns us we should fine-tune this model before using it for inference, which is exactly what we are going to do." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "FEfyuq1U8hDT" }, "source": [ "To instantiate an `IPUTrainer` class, we will need to define the training configuration and the evaluation metric. \n", "\n", "The most important is `IPUTrainingArguments`, which is a class that contains all the attributes to customize the training. It requires a folder name, which will be used to save the checkpoints of the model. All other arguments are optional:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "xc_MTm0Ks3DF" }, "outputs": [], "source": [ "model_name = model_checkpoint.split(\"/\")[-1]\n", "\n", "args = IPUTrainingArguments(\n", " f\"{model_name}-finetuned-ks\",\n", " evaluation_strategy = \"epoch\",\n", " save_strategy = \"epoch\",\n", " eval_delay = 2,\n", " learning_rate=3e-5,\n", " per_device_train_batch_size=per_device_train_batch_size,\n", " gradient_accumulation_steps=gradient_accumulation_steps,\n", " n_ipu=n_ipu,\n", " per_device_eval_batch_size=per_device_eval_batch_size,\n", " weight_decay=0.01,\n", " num_train_epochs=5,\n", " warmup_ratio=0.1,\n", " logging_steps=10,\n", " load_best_model_at_end=True,\n", " metric_for_best_model=\"accuracy\",\n", " push_to_hub=False,\n", " dataloader_num_workers=64,\n", " dataloader_drop_last=True,\n", " report_to=\"none\",\n", ")" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "xi6JYNYs8lJO" }, "source": [ "Here we set the evaluation to be done at the end of each epoch, tweak the learning rate, change the batch size by adjusting `gradient_accumulation_steps`, and customize the number of epochs for training, as well as the weight decay. Since the best model might not be the one at the end of training, we ask `IPUTrainer` to load the best model it saved (according to `metric_name`) at the end of training.\n", "\n", "`push_to_hub` is necessary if we want to push the model to the [🤗 Models Hub](https://huggingface.co/models) regularly during training. You can remove them if you didn't follow the installation steps at the beginning of this notebook. If you want to save your model locally to a name that is different to the name of the repository it will be pushed to, or if you want to push your model under an organization and not your name space, use the `hub_model_id` argument to set the repo name (it needs to be the full name, including your namespace: for instance `\"anton-l/hubert-finetuned-ks\"` or `\"huggingface/anton-l/hubert-finetuned-ks\"`)." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "2VE_HSha9RZk" }, "source": [ "Next, we need to define a function for how to compute the metrics from the predictions, which will just use `metric`, which we loaded earlier. The only preprocessing we have to do is to take the argmax of our predicted logits:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "EVWfiBuv2uCS" }, "outputs": [], "source": [ "def compute_metrics(eval_pred):\n", " \"\"\"Computes accuracy on a batch of predictions\"\"\"\n", " predictions = np.argmax(eval_pred.predictions, axis=1)\n", " return metric.compute(predictions=predictions, references=eval_pred.label_ids)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "To instantiate an `IPUTrainer` class, we will need to define `IPUConfig`, which is a class that specifies attributes and configuration parameters to compile and put the model on the device. We initialize it with a config name or path, which we set earlier:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ipu_config = IPUConfig.from_pretrained(\n", " ipu_config_name,\n", " executable_cache_dir=executable_cache_dir\n", ")" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "yTF0dWw49fB9" }, "source": [ "Then we pass all of this together with our datasets to `IPUTrainer`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "McVoaCPr3Cj-" }, "outputs": [], "source": [ "trainer = IPUTrainer(\n", " model,\n", " ipu_config,\n", " args,\n", " train_dataset=encoded_dataset[\"train\"],\n", " eval_dataset=encoded_dataset[\"validation\"],\n", " compute_metrics=compute_metrics,\n", ")" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "ltokP9mO9pjI" }, "source": [ "You might wonder why we pass along `feature_extractor` as a tokenizer when we already preprocessed our data. This is because we will use it again to make all the samples we gather the same length by applying padding, which requires knowing the model's preferences regarding padding (to pad to the left or right, and with which token to pad with). `feature_extractor` has a pad method that will do all of this for us, and `IPUTrainer` will use it. You can customize this part by defining and passing your own `data_collator` which will receive the samples like the dictionaries seen above and will need to return a dictionary of tensors." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "9j6VNsGP97LG" }, "source": [ "Now we can fine-tune our model by calling the `train` method:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "Pps61vF_4QaH", "outputId": "fc32c7ba-c478-4725-d06a-fc8ba54ddff0" }, "outputs": [], "source": [ "trainer.train()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "Vyb-58x_-A0e" }, "source": [ "We can check with the `evaluate` method that `trainer` did reload the best model properly (if it was not the last one):" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 190 }, "id": "niniUAnb5IrR", "outputId": "306c07b3-e482-4c62-a147-93fb6e16e4da" }, "outputs": [], "source": [ "trainer.evaluate()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "ymwN-SIR-NDF" }, "source": [ "You can upload the result of the training to the 🤗 Hub:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "4aNMErFz-GzX" }, "outputs": [], "source": [ "if trainer.args.push_to_hub:\n", " # trainer.push_to_hub()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "cZQnNUsI-Q4S" }, "source": [ "You can also share this model and other users can load it with the identifier \"your-username/the-name-you-picked\" so for instance:\n", "\n", "```python\n", "from transformers import AutoModelForAudioClassification, AutoFeatureExtractor\n", "\n", "feature_extractor = AutoFeatureExtractor.from_pretrained(\"anton-l/my-awesome-model\")\n", "model = AutoModelForAudioClassification.from_pretrained(\"anton-l/my-awesome-model\")\n", "```" ] }, { "attachments": 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