{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "g4JTHJ-IATho" }, "source": [ "# **Fine-tuning for Audio Classification with 🤗 Transformers**" ] }, { "cell_type": "markdown", "metadata": { "id": "o-CI6ON8AMcc" }, "source": [ "This notebook shows how to fine-tune multi-lingual pretrained speech models for Automatic Speech Recognition." ] }, { "cell_type": "markdown", "metadata": { "id": "pdcMxVGEA9Cd" }, "source": [ "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 [Model 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 (e.g. [Wav2Vec2ForSequenceClassification](https://huggingface.co/transformers/model_doc/wav2vec2.html#wav2vec2forsequenceclassification)). \n", "\n", "Depending on the model and the GPU you are using, you might need to adjust the batch size to avoid out-of-memory errors. Set those two parameters, then the rest of the notebook should run smoothly:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "5WMEawzyCEyG" }, "outputs": [], "source": [ "model_checkpoint = \"facebook/wav2vec2-base\"\n", "batch_size = 32" ] }, { "cell_type": "markdown", "metadata": { "id": "NlArTG8KChJf" }, "source": [ "Before we start, let's install both `datasets` and `transformers` from master. Also, we need the `librosa` package to load audio files." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "L1532RVbJgQV" }, "outputs": [], "source": [ "%%capture\n", "!pip install datasets==1.14\n", "!pip install transformers==4.11.3\n", "!pip install librosa" ] }, { "cell_type": "markdown", "metadata": { "id": "snZ1tmaOC412" }, "source": [ "If you're opening this notebook locally, make sure your environment has an install from the last version of those libraries.\n", "\n", "To be able to share your model with the community and generate results like the one shown in the picture below via the inference API, there are a few more steps to follow.\n", "\n", "First you have to store your authentication token from the Hugging Face website (sign up [here](https://huggingface.co/join) if you haven't already!) then 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": 4, "metadata": { "id": "t7JEhLjDDcVd" }, "outputs": [], "source": [ "%%capture\n", "!apt install git-lfs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We also quickly upload some telemetry - this tells us which examples and software versions are getting used so we know where to prioritize our maintenance efforts. We don't collect (or care about) any personally identifiable information, but if you'd prefer not to be counted, feel free to skip this step or delete this cell entirely." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from transformers.utils import send_example_telemetry\n", "\n", "send_example_telemetry(\"audio_classification_notebook\", framework=\"pytorch\")" ] }, { "cell_type": "markdown", "metadata": { "id": "XalxdrirGkLl" }, "source": [ "## Fine-tuning a model on an audio classification task" ] }, { "cell_type": "markdown", "metadata": { "id": "cnRWZSq0GRRh" }, "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 of the [SUPERB Benchmark](https://superbbenchmark.org/)\n", "\n", "Keyword Spotting (KS) detects preregistered keywords by classifying utterances 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": { "id": "lu8Od7EgFLwe" }, "source": [ "\"drawing\"" ] }, { "cell_type": "markdown", "metadata": { "id": "mcE455KaG687" }, "source": [ "### Loading the dataset" ] }, { "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 need to use for evaluation. This can be easily done with the functions `load_dataset` and `load_metric`. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "id": "fhVQU42BLd3p" }, "outputs": [], "source": [ "from datasets import load_dataset, load_metric" ] }, { "cell_type": "code", "execution_count": 6, "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", 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"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": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4b35a346a5624e0d805105529763b59f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading: 0%| | 0.00/7.54k [00:00\n", " \n", " Your browser does not support the audio element.\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Label: down\n", "Shape: (16000,), sampling rate: 16000\n" ] }, { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Label: _unknown_\n", "Shape: (16000,), sampling rate: 16000\n" ] }, { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Label: go\n", "Shape: (16000,), sampling rate: 16000\n" ] }, { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Label: _unknown_\n", "Shape: (15604,), sampling rate: 16000\n" ] }, { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "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()" ] }, { "cell_type": "markdown", "metadata": { "id": "37K_0FAQoNRM" }, "source": [ "If you run the cell a couple of 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`." ] }, { "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": 14, "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": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2fafd1b4c6c5483f9922bef1395dba65", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading: 0%| | 0.00/1.80k [00:00\n", " \n", " \n", " [1995/1995 1:11:18, Epoch 4/5]\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
EpochTraining LossValidation LossAccuracy
00.7908000.6775990.900853
10.3202000.2061190.976317
20.2210000.1256910.978523
30.1773000.0990140.981318
40.1729000.0951610.982348

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "***** Running Evaluation *****\n", " Num examples = 6798\n", " Batch size = 32\n", "Saving model checkpoint to wav2vec2-base-finetuned-ks/checkpoint-399\n", "Configuration saved in wav2vec2-base-finetuned-ks/checkpoint-399/config.json\n", "Model weights saved in wav2vec2-base-finetuned-ks/checkpoint-399/pytorch_model.bin\n", "Configuration saved in wav2vec2-base-finetuned-ks/checkpoint-399/preprocessor_config.json\n", "Configuration saved in wav2vec2-base-finetuned-ks/preprocessor_config.json\n", "***** Running Evaluation *****\n", " Num examples = 6798\n", " Batch size = 32\n", "Saving model checkpoint to wav2vec2-base-finetuned-ks/checkpoint-798\n", "Configuration saved in wav2vec2-base-finetuned-ks/checkpoint-798/config.json\n", "Model weights saved in wav2vec2-base-finetuned-ks/checkpoint-798/pytorch_model.bin\n", "Configuration saved in wav2vec2-base-finetuned-ks/checkpoint-798/preprocessor_config.json\n", "***** Running Evaluation *****\n", " Num examples = 6798\n", " Batch size = 32\n", "Saving model checkpoint to wav2vec2-base-finetuned-ks/checkpoint-1197\n", "Configuration saved in wav2vec2-base-finetuned-ks/checkpoint-1197/config.json\n", "Model weights saved in wav2vec2-base-finetuned-ks/checkpoint-1197/pytorch_model.bin\n", "Configuration saved in wav2vec2-base-finetuned-ks/checkpoint-1197/preprocessor_config.json\n", "***** Running Evaluation *****\n", " Num examples = 6798\n", " Batch size = 32\n", "Saving model checkpoint to wav2vec2-base-finetuned-ks/checkpoint-1596\n", "Configuration saved in wav2vec2-base-finetuned-ks/checkpoint-1596/config.json\n", "Model weights saved in wav2vec2-base-finetuned-ks/checkpoint-1596/pytorch_model.bin\n", "Configuration saved in wav2vec2-base-finetuned-ks/checkpoint-1596/preprocessor_config.json\n", "***** Running Evaluation *****\n", " Num examples = 6798\n", " Batch size = 32\n", "Saving model checkpoint to wav2vec2-base-finetuned-ks/checkpoint-1995\n", "Configuration saved in wav2vec2-base-finetuned-ks/checkpoint-1995/config.json\n", "Model weights saved in wav2vec2-base-finetuned-ks/checkpoint-1995/pytorch_model.bin\n", "Configuration saved in wav2vec2-base-finetuned-ks/checkpoint-1995/preprocessor_config.json\n", "\n", "\n", "Training completed. Do not forget to share your model on huggingface.co/models =)\n", "\n", "\n", "Loading best model from wav2vec2-base-finetuned-ks/checkpoint-1995 (score: 0.9823477493380406).\n" ] }, { "data": { "text/plain": [ "TrainOutput(global_step=1995, training_loss=0.5248636143846919, metrics={'train_runtime': 4281.3853, 'train_samples_per_second': 59.67, 'train_steps_per_second': 0.466, 'total_flos': 2.31918157475328e+18, 'train_loss': 0.5248636143846919, 'epoch': 5.0})" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trainer.train()" ] }, { "cell_type": "markdown", "metadata": { "id": "Vyb-58x_-A0e" }, "source": [ "We can check with the `evaluate` method that our `Trainer` did reload the best model properly (if it was not the last one):" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 190 }, "id": "niniUAnb5IrR", "outputId": "306c07b3-e482-4c62-a147-93fb6e16e4da" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "***** Running Evaluation *****\n", " Num examples = 6798\n", " Batch size = 32\n" ] }, { "data": { "text/html": [ "\n", "

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\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "{'epoch': 5.0,\n", " 'eval_accuracy': 0.9823477493380406,\n", " 'eval_loss': 0.09516120702028275,\n", " 'eval_runtime': 68.0133,\n", " 'eval_samples_per_second': 99.951,\n", " 'eval_steps_per_second': 3.132}" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trainer.evaluate()" ] }, { "cell_type": "markdown", "metadata": { "id": "ymwN-SIR-NDF" }, "source": [ "You can now upload the result of the training to the Hub, just execute this instruction:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "4aNMErFz-GzX" }, "outputs": [], "source": [ "trainer.push_to_hub()" ] }, { "cell_type": "markdown", "metadata": { "id": "cZQnNUsI-Q4S" }, "source": [ "You can now share this model with all your friends, family, favorite pets: they can all 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", "\n", "```" ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [], "name": "Audio-Classification-on-Keyword-Spotting.ipynb", "provenance": [], "toc_visible": true }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.8" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "02e5f5185d7e4d8789485b0128a15063": { "model_module": "@jupyter-widgets/controls", "model_module_version": 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