{
"cells": [
{
"cell_type": "markdown",
"id": "75b58048-7d14-4fc6-8085-1fc08c81b4a6",
"metadata": {
"id": "75b58048-7d14-4fc6-8085-1fc08c81b4a6"
},
"source": [
"# Fine-Tune Whisper With 🤗 Transformers and Streaming Mode"
]
},
{
"cell_type": "markdown",
"id": "fbfa8ad5-4cdc-4512-9058-836cbbf65e1a",
"metadata": {
"id": "fbfa8ad5-4cdc-4512-9058-836cbbf65e1a"
},
"source": [
"In this Colab, we present a step-by-step guide on fine-tuning Whisper with Hugging Face 🤗 Transformers on 400 hours of speech data! Using streaming mode, we'll show how you can train a speech recongition model on any dataset, irrespective of size. With streaming mode, storage requirements are no longer a consideration: you can train a model on whatever dataset you want, even if it's download size exceeds your devices disk space. How can this be possible? It simply seems too good to be true! Well, rest assured it's not 😉 Carry on reading to find out more."
]
},
{
"cell_type": "markdown",
"id": "afe0d503-ae4e-4aa7-9af4-dbcba52db41e",
"metadata": {
"id": "afe0d503-ae4e-4aa7-9af4-dbcba52db41e"
},
"source": [
"## Introduction"
]
},
{
"cell_type": "markdown",
"id": "9ae91ed4-9c3e-4ade-938e-f4c2dcfbfdc0",
"metadata": {
"id": "9ae91ed4-9c3e-4ade-938e-f4c2dcfbfdc0"
},
"source": [
"Speech recognition datasets are large. A typical speech dataset consists of approximately 100 hours of audio-transcription data, requiring upwards of 130GB of storage space for download and preparation. For most ASR researchers, this is already at the upper limit of what is feasible for disk space. So what happens when we want to train on a larger dataset? The full [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) dataset consists of 960 hours of audio data. Kensho's [SPGISpeech](https://huggingface.co/datasets/kensho/spgispeech) contains 5,000 hours of audio data. ML Commons [People's Speech](https://huggingface.co/datasets/MLCommons/peoples_speech) contains **30,000+** hours of audio data! Do we need to bite the bullet and buy additional storage? Or is there a way we can train on all of these datasets with no disk drive requirements?\n",
"\n",
"When training machine learning systems, we rarely use the entire dataset at once. We typically _batch_ our data into smaller subsets of data, and pass these incrementally through our training pipeline. This is because we train our system on an accelerator device, such as a GPU or TPU, which has a memory limit typically around 16GB. We have to fit our model, optimiser and training data all on the same accelerator device, so we usually have to divide the dataset up into smaller batches and move them from the CPU to the GPU when required.\n",
"\n",
"Consequently, we don't require the entire dataset to be downloaded at once; we simply need the batch of data that we pass to our model at any one go. We can leverage this principle of partial dataset loading when preparing our dataset: rather than downloading the entire dataset at the start, we can load each piece of data as and when we need it. For each batch, we load the relevant data from a remote server and pass it through the training pipeline. For the next batch, we load the next items and again pass them through the training pipeline. At no point do we have to save data to our disk drive, we simply load them in memory and use them in our pipeline. In doing so, we only ever need as much memory as each individual batch requires.\n",
"\n",
"This is analogous to downloading a TV show versus streaming it 📺 When we download a TV show, we download the entire video offline and save it to our disk. Compare this to when we stream a TV show. Here, we don't download any part of the video to memory, but iterate over the video file and load each part in real-time as required. It's this same principle that we can apply to our ML training pipeline! We want to iterate over the dataset and load each sample of data as required.\n",
"\n",
"While the principle of partial dataset loading sounds ideal, it also seems **pretty** difficult to do. Luckily for us, 🤗 Datasets allows us to do this with minimal code changes! We'll make use of the principle of [_streaming_](https://huggingface.co/docs/datasets/stream), depicted graphically in Figure 1. Streaming does exactly this: the data is loaded progressively as we iterate over the dataset, meaning it is only loaded as and when we need it. If you're familiar with 🤗 Transformers and Datasets, the content of this notebook will be very familiar, with some small extensions to support streaming mode."
]
},
{
"cell_type": "markdown",
"id": "1c87f76e-47be-4a5d-bc52-7b1c2e9d4f5a",
"metadata": {
"id": "1c87f76e-47be-4a5d-bc52-7b1c2e9d4f5a"
},
"source": [
"\n",
"Figure 1: Streaming mode. The dataset is divided into smaller subsets, with subsets loaded progressively as we iterate over the dataset. "
]
},
{
"cell_type": "markdown",
"id": "d44b85a2-3465-4cd5-bcca-8ddb302ab71b",
"metadata": {
"id": "d44b85a2-3465-4cd5-bcca-8ddb302ab71b",
"tags": []
},
"source": [
"## Prepare Environment"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "a0e8a3b5-2c0b-4ee6-98cc-21a571266a5d",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "a0e8a3b5-2c0b-4ee6-98cc-21a571266a5d",
"outputId": "09b1863a-eb05-4610-b763-2a7b69cd77bf"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reading package lists... Done\n",
"Building dependency tree \n",
"Reading state information... Done\n",
"git-lfs is already the newest version (2.9.2-1).\n",
"0 upgraded, 0 newly installed, 0 to remove and 154 not upgraded.\n",
"Error: Failed to call git rev-parse --git-dir: exit status 128 \n",
"Git LFS initialized.\n"
]
}
],
"source": [
"!sudo apt-get install git-lfs\n",
"!sudo git lfs install\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "QJBETye7FkvV",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "QJBETye7FkvV",
"outputId": "e055cc0a-0a62-4a14-f360-2a64782a5a35"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Defaulting to user installation because normal site-packages is not writeable\n",
"Requirement already satisfied: pip in ./.local/lib/python3.8/site-packages (22.3.1)\n",
"Defaulting to user installation because normal site-packages is not writeable\n",
"Requirement already satisfied: numpy<1.23.0 in ./.local/lib/python3.8/site-packages (1.22.4)\n",
"Defaulting to user installation because normal site-packages is not writeable\n",
"Collecting torch\n",
" Using cached torch-1.13.0-cp38-cp38-manylinux1_x86_64.whl (890.2 MB)\n",
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"\u001b[?25hCollecting nvidia-cuda-runtime-cu11==11.7.99\n",
" Using cached nvidia_cuda_runtime_cu11-11.7.99-py3-none-manylinux1_x86_64.whl (849 kB)\n",
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"Collecting nvidia-cublas-cu11==11.10.3.66\n",
" Using cached nvidia_cublas_cu11-11.10.3.66-py3-none-manylinux1_x86_64.whl (317.1 MB)\n",
"Collecting setuptools\n",
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"Collecting numpy\n",
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"\u001b[?25hInstalling collected packages: wheel, urllib3, typing-extensions, setuptools, pillow, nvidia-cuda-nvrtc-cu11, numpy, idna, charset-normalizer, certifi, requests, nvidia-cuda-runtime-cu11, nvidia-cublas-cu11, nvidia-cudnn-cu11, torch, torchvision, torchaudio\n",
" Attempting uninstall: typing-extensions\n",
" Found existing installation: typing_extensions 4.4.0\n",
" Uninstalling typing_extensions-4.4.0:\n",
" Successfully uninstalled typing_extensions-4.4.0\n",
" Attempting uninstall: nvidia-cuda-nvrtc-cu11\n",
" Found existing installation: nvidia-cuda-nvrtc-cu11 11.7.99\n",
" Uninstalling nvidia-cuda-nvrtc-cu11-11.7.99:\n",
" Successfully uninstalled nvidia-cuda-nvrtc-cu11-11.7.99\n",
" Attempting uninstall: numpy\n",
" Found existing installation: numpy 1.22.4\n",
" Uninstalling numpy-1.22.4:\n",
" Successfully uninstalled numpy-1.22.4\n",
" Attempting uninstall: charset-normalizer\n",
" Found existing installation: charset-normalizer 2.1.1\n",
" Uninstalling charset-normalizer-2.1.1:\n",
" Successfully uninstalled charset-normalizer-2.1.1\n",
" Attempting uninstall: requests\n",
" Found existing installation: requests 2.28.1\n",
" Uninstalling requests-2.28.1:\n",
" Successfully uninstalled requests-2.28.1\n",
" Attempting uninstall: nvidia-cuda-runtime-cu11\n",
" Found existing installation: nvidia-cuda-runtime-cu11 11.7.99\n",
" Uninstalling nvidia-cuda-runtime-cu11-11.7.99:\n",
" Successfully uninstalled nvidia-cuda-runtime-cu11-11.7.99\n",
" Attempting uninstall: nvidia-cublas-cu11\n",
" Found existing installation: nvidia-cublas-cu11 11.10.3.66\n",
" Uninstalling nvidia-cublas-cu11-11.10.3.66:\n",
" Successfully uninstalled nvidia-cublas-cu11-11.10.3.66\n",
" Attempting uninstall: nvidia-cudnn-cu11\n",
" Found existing installation: nvidia-cudnn-cu11 8.5.0.96\n",
" Uninstalling nvidia-cudnn-cu11-8.5.0.96:\n",
" Successfully uninstalled nvidia-cudnn-cu11-8.5.0.96\n",
" Attempting uninstall: torch\n",
" Found existing installation: torch 1.13.0\n",
" Uninstalling torch-1.13.0:\n",
" Successfully uninstalled torch-1.13.0\n",
" Attempting uninstall: torchaudio\n",
" Found existing installation: torchaudio 0.13.0\n",
" Uninstalling torchaudio-0.13.0:\n",
" Successfully uninstalled torchaudio-0.13.0\n",
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"launchpadlib 1.10.13 requires testresources, which is not installed.\n",
"pandas-profiling 3.4.0 requires numpy<1.24,>=1.16.0, but you have numpy 1.24.0rc2 which is incompatible.\n",
"numba 0.56.4 requires numpy<1.24,>=1.18, but you have numpy 1.24.0rc2 which is incompatible.\u001b[0m\u001b[31m\n",
"\u001b[0mSuccessfully installed certifi-2022.12.7 charset-normalizer-2.1.1 idna-3.4 numpy-1.24.0rc2 nvidia-cublas-cu11-11.10.3.66 nvidia-cuda-nvrtc-cu11-11.7.99 nvidia-cuda-runtime-cu11-11.7.99 nvidia-cudnn-cu11-8.5.0.96 pillow-9.3.0 requests-2.28.1 setuptools-65.6.3 torch-1.13.0 torchaudio-0.13.0 torchvision-0.14.0 typing-extensions-4.4.0 urllib3-1.26.13 wheel-0.38.4\n"
]
}
],
"source": [
"!pip3 install --upgrade pip\n",
"!pip3 install \"numpy<1.23.0\"\n",
"\n",
"!pip3 install --pre torch torchaudio torchvision --force-reinstall\n",
"\n",
"!pip3 install bitsandbytes\n",
"\n",
"\n",
"#!pip3 install --pre torch torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cu116\n",
"#!pip3 install numpy --pre torch[dynamo] torchvision torchaudio --force-reinstall --extra-index-url https://download.pytorch.org/whl/nightly/cu116\n",
"#!pip3 install numpy --pre torch[dynamo] torchaudio --force-reinstall --extra-index-url https://download.pytorch.org/whl/nightly/cu116\n",
"\n",
"#!pip3 install numpy --pre torch[dynamo] torchaudio --upgrade --extra-index-url https://download.pytorch.org/whl/nightly/cu117\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "a47bbac5-b44b-41ac-a948-1b57cec2b6f1",
"metadata": {
"id": "a47bbac5-b44b-41ac-a948-1b57cec2b6f1"
},
"source": [
"First of all, let's try to secure a decent GPU for our Colab! Unfortunately, it's becoming much harder to get access to a good GPU with the free version of Google Colab. However, with Google Colab Pro / Pro+ one should have no issues in being allocated a V100 or P100 GPU.\n",
"\n",
"To get a GPU, click _Runtime_ -> _Change runtime type_, then change _Hardware accelerator_ from _None_ to _GPU_."
]
},
{
"cell_type": "markdown",
"id": "47686bd5-cbb1-4352-81cf-0fcf7bbd45c3",
"metadata": {
"id": "47686bd5-cbb1-4352-81cf-0fcf7bbd45c3"
},
"source": [
"We can verify that we've been assigned a GPU and view its specifications:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "d74b38c5-a1fb-4214-b4f4-b5bf0869f169",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "d74b38c5-a1fb-4214-b4f4-b5bf0869f169",
"outputId": "18ca6853-0836-4cba-f06a-02fe1cecd715",
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Thu Dec 8 18:45:37 2022 \n",
"+-----------------------------------------------------------------------------+\n",
"| NVIDIA-SMI 515.65.01 Driver Version: 515.65.01 CUDA Version: 11.7 |\n",
"|-------------------------------+----------------------+----------------------+\n",
"| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
"| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n",
"| | | MIG M. |\n",
"|===============================+======================+======================|\n",
"| 0 NVIDIA A100-SXM... On | 00000000:06:00.0 Off | 0 |\n",
"| N/A 31C P0 47W / 400W | 0MiB / 40960MiB | 0% Default |\n",
"| | | Disabled |\n",
"+-------------------------------+----------------------+----------------------+\n",
" \n",
"+-----------------------------------------------------------------------------+\n",
"| Processes: |\n",
"| GPU GI CI PID Type Process name GPU Memory |\n",
"| ID ID Usage |\n",
"|=============================================================================|\n",
"| No running processes found |\n",
"+-----------------------------------------------------------------------------+\n"
]
}
],
"source": [
"gpu_info = !nvidia-smi\n",
"gpu_info = '\\n'.join(gpu_info)\n",
"if gpu_info.find('failed') >= 0:\n",
" print('Not connected to a GPU')\n",
"else:\n",
" print(gpu_info)"
]
},
{
"cell_type": "markdown",
"id": "be67f92a-2f3b-4941-a1c0-5ed2de6e0a6a",
"metadata": {
"id": "be67f92a-2f3b-4941-a1c0-5ed2de6e0a6a",
"tags": []
},
"source": [
"Next, we need to update the Unix package `ffmpeg` to version 4:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "15493a84-8b7c-4b35-9aeb-2b0a57a4e937",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "15493a84-8b7c-4b35-9aeb-2b0a57a4e937",
"outputId": "9463f72d-a888-4980-abc4-2b6a2ece61b2",
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Get:1 https://nvidia.github.io/libnvidia-container/stable/ubuntu18.04/amd64 InRelease [1484 B]\n",
"Hit:2 https://nvidia.github.io/nvidia-container-runtime/stable/ubuntu18.04/amd64 InRelease\n",
"Hit:3 https://download.docker.com/linux/ubuntu focal InRelease \n",
"Hit:4 http://archive.lambdalabs.com/ubuntu focal InRelease \n",
"Hit:5 https://packages.cloud.google.com/apt cloud-sdk InRelease \n",
"Hit:6 http://security.ubuntu.com/ubuntu focal-security InRelease \n",
"Ign:7 http://ppa.launchpad.net/jonathonf/ffmpeg-4/ubuntu focal InRelease \n",
"Hit:8 http://archive.ubuntu.com/ubuntu focal InRelease \n",
"Hit:9 https://packages.microsoft.com/repos/azure-cli focal InRelease \n",
"Hit:10 http://archive.ubuntu.com/ubuntu focal-updates InRelease \n",
"Hit:11 https://pkg.cloudflare.com/cloudflared focal InRelease \n",
"Hit:12 http://archive.ubuntu.com/ubuntu focal-backports InRelease \n",
"Err:13 http://ppa.launchpad.net/jonathonf/ffmpeg-4/ubuntu focal Release \n",
" 404 Not Found [IP: 185.125.190.52 80]\n",
"Hit:14 https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu focal InRelease \n",
"Reading package lists... Done\n",
"E: The repository 'http://ppa.launchpad.net/jonathonf/ffmpeg-4/ubuntu focal Release' does not have a Release file.\n",
"N: Updating from such a repository can't be done securely, and is therefore disabled by default.\n",
"N: See apt-secure(8) manpage for repository creation and user configuration details.\n",
"Get:1 https://nvidia.github.io/libnvidia-container/stable/ubuntu18.04/amd64 InRelease [1484 B]\n",
"Hit:2 https://download.docker.com/linux/ubuntu focal InRelease \u001b[0m\u001b[33m\n",
"Hit:3 https://nvidia.github.io/nvidia-container-runtime/stable/ubuntu18.04/amd64 InRelease\n",
"Hit:4 https://packages.cloud.google.com/apt cloud-sdk InRelease \u001b[0m\u001b[33m\u001b[33m\n",
"Hit:5 http://archive.lambdalabs.com/ubuntu focal InRelease \u001b[0m\n",
"Hit:6 http://archive.ubuntu.com/ubuntu focal InRelease \u001b[0m\u001b[33m\n",
"Hit:7 http://security.ubuntu.com/ubuntu focal-security InRelease \u001b[0m\n",
"Ign:8 http://ppa.launchpad.net/jonathonf/ffmpeg-4/ubuntu focal InRelease \u001b[0m\n",
"Hit:9 https://packages.microsoft.com/repos/azure-cli focal InRelease \u001b[0m\u001b[33m\n",
"Hit:10 http://archive.ubuntu.com/ubuntu focal-updates InRelease \u001b[0m\n",
"Hit:11 https://pkg.cloudflare.com/cloudflared focal InRelease \u001b[0m\u001b[33m\n",
"Hit:12 http://archive.ubuntu.com/ubuntu focal-backports InRelease \u001b[0m \u001b[0m\u001b[33m\n",
"Hit:13 https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu focal InRelease\n",
"Err:14 http://ppa.launchpad.net/jonathonf/ffmpeg-4/ubuntu focal Release\n",
" 404 Not Found [IP: 185.125.190.52 80]\n",
"Reading package lists... Done\u001b[33m\n",
"\u001b[1;31mE: \u001b[0mThe repository 'http://ppa.launchpad.net/jonathonf/ffmpeg-4/ubuntu focal Release' does not have a Release file.\u001b[0m\n",
"\u001b[33mN: \u001b[0mUpdating from such a repository can't be done securely, and is therefore disabled by default.\u001b[0m\n",
"\u001b[33mN: \u001b[0mSee apt-secure(8) manpage for repository creation and user configuration details.\u001b[0m\n",
"Reading package lists... Done\n",
"Building dependency tree \n",
"Reading state information... Done\n",
"ffmpeg is already the newest version (7:4.2.7-0ubuntu0.1).\n",
"0 upgraded, 0 newly installed, 0 to remove and 154 not upgraded.\n"
]
}
],
"source": [
"!sudo add-apt-repository -y ppa:jonathonf/ffmpeg-4\n",
"!sudo apt update\n",
"!sudo apt install -y ffmpeg"
]
},
{
"cell_type": "markdown",
"id": "ab471347-a547-4d14-9d11-f151dc9547a7",
"metadata": {
"id": "ab471347-a547-4d14-9d11-f151dc9547a7"
},
"source": [
"We'll employ several popular Python packages to fine-tune the Whisper model.\n",
"We'll use `datasets` to download and prepare our training data and \n",
"`transformers` to load and train our Whisper model. We'll also require\n",
"the `soundfile` package to pre-process audio files, `evaluate` and `jiwer` to\n",
"assess the performance of our model. Finally, we'll\n",
"use `gradio` to build a flashy demo of our fine-tuned model."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "4e106846-3620-46aa-989d-5e35e27c8057",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "4e106846-3620-46aa-989d-5e35e27c8057",
"outputId": "6bcef5d6-c7de-45de-abd4-ab5883dfaab4"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Defaulting to user installation because normal site-packages is not writeable\n",
"Collecting git+https://github.com/huggingface/datasets\n",
" Cloning https://github.com/huggingface/datasets to /tmp/pip-req-build-_aqg2yxr\n",
" Running command git clone --filter=blob:none --quiet https://github.com/huggingface/datasets /tmp/pip-req-build-_aqg2yxr\n",
" Resolved https://github.com/huggingface/datasets to commit 45508f7d8858579c62d93779873ef5eb6b05bc74\n",
" Installing build dependencies ... \u001b[?25ldone\n",
"\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n",
"\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n",
"\u001b[?25hRequirement already satisfied: dill<0.3.7 in /usr/local/lib/python3.8/dist-packages (from datasets==2.7.1.dev0) (0.3.6)\n",
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"Defaulting to user installation because normal site-packages is not writeable\n",
"Collecting git+https://github.com/huggingface/transformers\n",
" Cloning https://github.com/huggingface/transformers to /tmp/pip-req-build-x539p2ep\n",
" Running command git clone --filter=blob:none --quiet https://github.com/huggingface/transformers /tmp/pip-req-build-x539p2ep\n",
" Resolved https://github.com/huggingface/transformers to commit e3cc4487fe66e03ec85970ea2db8e5fb34c455f4\n",
" Installing build dependencies ... \u001b[?25ldone\n",
"\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n",
"\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n",
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"Defaulting to user installation because normal site-packages is not writeable\n",
"Requirement already satisfied: more-itertools in /usr/lib/python3/dist-packages (4.2.0)\n"
]
}
],
"source": [
"!pip install git+https://github.com/huggingface/datasets\n",
"!pip install git+https://github.com/huggingface/transformers\n",
"!pip3 install numexpr>=2.7.1\n",
"!pip install librosa\n",
"!pip install evaluate>=0.3.0\n",
"!pip install jiwer\n",
"!pip install gradio\n",
"!pip install more-itertools"
]
},
{
"cell_type": "markdown",
"id": "5b185650-af09-48c6-a67b-0e4368b74b3b",
"metadata": {
"id": "5b185650-af09-48c6-a67b-0e4368b74b3b",
"tags": []
},
"source": [
"Linking the notebook to the Hugging Face Hub is straightforward - it simply \n",
"\n",
"\n",
"requires entering your \n",
"Hub authentication token when prompted. Find your Hub authentication token [here](https://huggingface.co/settings/tokens):"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "dff27c76-575c-432b-8916-b1b810efef4a",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 331,
"referenced_widgets": [
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"3ae98fe05f88443d821d5df7488a123b",
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"id": "dff27c76-575c-432b-8916-b1b810efef4a",
"outputId": "5beb152e-9d4f-4581-8063-c5752890c4fa"
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cee65d4b203d4b2a910d65aba8ff273c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(HTML(value=' IterableDataset:\n",
"\n",
" if len(dataset_names) != len(dataset_config_names):\n",
" raise ValueError(\n",
" f\"Ensure one config is passed for each dataset, got {len(dataset_names)} datasets and\"\n",
" f\" {len(dataset_config_names)} configs.\"\n",
" )\n",
"\n",
" if splits is not None and len(splits) != len(dataset_names):\n",
" raise ValueError(\n",
" f\"Ensure one split is passed for each dataset, got {len(dataset_names)} datasets and {len(splits)} splits.\"\n",
" )\n",
"\n",
" if text_column_names is not None and len(text_column_names) != len(dataset_names):\n",
" raise ValueError(\n",
" f\"Ensure one text column name is passed for each dataset, got {len(dataset_names)} datasets and\"\n",
" f\" {len(text_column_names)} text column names.\"\n",
" )\n",
"\n",
" splits = splits if splits is not None else [\"train\" for i in range(len(dataset_names))]\n",
" text_column_names = (\n",
" text_column_names if text_column_names is not None else [\"text\" for i in range(len(dataset_names))]\n",
" )\n",
"\n",
" all_datasets = []\n",
" # iterate over the datasets we want to interleave\n",
" for i, dataset_name in enumerate(dataset_names):\n",
" dataset = load_dataset(dataset_name, dataset_config_names[i], split=splits[i], streaming=True, **kwargs)\n",
" # resample to specified sampling rate\n",
" dataset = dataset.cast_column(\"audio\", Audio(sampling_rate))\n",
" # normalise columns to [\"audio\", \"sentence\"]\n",
" if text_column_names[i] != \"sentence\":\n",
" dataset = dataset.rename_column(text_column_names[i], \"sentence\")\n",
" dataset = dataset.remove_columns(set(dataset.features.keys()) - set([\"audio\", \"sentence\"]))\n",
" all_datasets.append(dataset)\n",
"\n",
" interleaved_dataset = interleave_datasets(all_datasets, stopping_strategy=stopping_strategy)\n",
" return interleaved_dataset\n",
"\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "qOwlctMhNmCG",
"metadata": {
"id": "qOwlctMhNmCG",
"tags": []
},
"outputs": [],
"source": [
"ds = load_multiple_streaming_datasets(dataset_names, dataset_config_names=dataset_config_names, text_column_names=text_column_names, use_auth_token=True)\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "2d63b2d2-f68a-4d74-b7f1-5127f6d16605",
"metadata": {
"id": "2d63b2d2-f68a-4d74-b7f1-5127f6d16605"
},
"source": [
"## Prepare Processor and Pre-Process Data"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "imRHJOpm4V_j",
"metadata": {
"id": "imRHJOpm4V_j"
},
"outputs": [],
"source": [
"from datasets import IterableDatasetDict\n",
"raw_datasets = IterableDatasetDict()\n",
"\n",
"raw_datasets[\"train\"] = load_multiple_streaming_datasets(dataset_names, dataset_config_names=dataset_config_names, text_column_names=text_column_names, use_auth_token=True)\n",
"raw_datasets[\"test\"] = load_dataset(\"mozilla-foundation/common_voice_11_0\", \"el\", split=\"test\", streaming=True, use_auth_token=True)\n",
"\n",
"#raw_datasets[\"train\"] = load_multiple_streaming_datasets(dataset_names, \"el\", split=\"\", use_auth_token=True)\n",
"#raw_datasets[\"test\"] = load_multiple_streaming_datasets(dataset_names, \"el\", split=\"\", use_auth_token=True)\n",
"\n",
"# dataset_config_names = [\"el\", \"el_gr\"]\n",
"\n",
"#raw_datasets[\"train\"] = load_multiple_streaming_datasets(dataset_names, \"el\", use_auth_token=True)\n",
"#raw_datasets[\"train\"] = load_multiple_streaming_datasets(dataset_names, \"el\", splits=[\"train\", \"train\"], use_auth_token=True)\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "601c3099-1026-439e-93e2-5635b3ba5a73",
"metadata": {
"id": "601c3099-1026-439e-93e2-5635b3ba5a73"
},
"source": [
"The ASR pipeline can be de-composed into three stages: \n",
"1) A feature extractor which pre-processes the raw audio-inputs\n",
"2) The model which performs the sequence-to-sequence mapping \n",
"3) A tokenizer which post-processes the model outputs to text format\n",
"\n",
"In 🤗 Transformers, the Whisper model has an associated feature extractor and tokenizer, \n",
"called [WhisperFeatureExtractor](https://huggingface.co/docs/transformers/main/model_doc/whisper#transformers.WhisperFeatureExtractor)\n",
"and [WhisperTokenizer](https://huggingface.co/docs/transformers/main/model_doc/whisper#transformers.WhisperTokenizer) \n",
"respectively. To make our lives simple, these two objects are wrapped under a single class, called the [WhisperProcessor](https://huggingface.co/docs/transformers/model_doc/whisper#transformers.WhisperProcessor). We can call the WhisperProcessor to perform \n",
"both the audio pre-processing and the text token post-processing. In doing so, we only need to keep track of two objects during training: \n",
"the `processor` and the `model`.\n",
"\n",
"If using a multilingual checkpoint, you should set the `\"language\"` to your target text language. You should also set the task to `\"transcribe\"` for speech recogntition and `\"translate\"` for speech translation. These arguments modify the behaviour of the tokenizer - they should be set correctly to ensure the target labels are encoded properly. These arguments should be omitted for English-only fine-tuning."
]
},
{
"cell_type": "code",
"execution_count": 11,
"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(\"emilios/whisper-medium-el\", language=\"Greek\", task=\"transcribe\")\n",
"#processor = WhisperProcessor.from_pretrained(\"farsipal/whisper-small-el\", language=\"Greek\", task=\"transcribe\")\n"
]
},
{
"cell_type": "markdown",
"id": "381acd09-0b0f-4d04-9eb3-f028ac0e5f2c",
"metadata": {
"id": "381acd09-0b0f-4d04-9eb3-f028ac0e5f2c"
},
"source": [
"### Pre-Process Data"
]
},
{
"cell_type": "markdown",
"id": "bf10cd3e-924e-44fc-8790-46e413de7b3d",
"metadata": {
"id": "bf10cd3e-924e-44fc-8790-46e413de7b3d"
},
"source": [
"Let's have a look at the dataset features. Pay particular attention to the `\"audio\"` column - this details the sampling rate of our audio inputs:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "ab5a13b4-9bd4-4aa0-aef2-b3de9b762988",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ab5a13b4-9bd4-4aa0-aef2-b3de9b762988",
"outputId": "10050f16-519c-4211-9e80-0f4928a3559d"
},
"outputs": [
{
"data": {
"text/plain": [
"{'audio': Audio(sampling_rate=16000, mono=True, decode=True, id=None),\n",
" 'sentence': Value(dtype='string', id=None)}"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_datasets[\"train\"].features"
]
},
{
"cell_type": "markdown",
"id": "5a679f05-063d-41b3-9b58-4fc9c6ccf4fd",
"metadata": {
"id": "5a679f05-063d-41b3-9b58-4fc9c6ccf4fd"
},
"source": [
"Since 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": 13,
"id": "3ab6a724-3d1e-478b-a9e9-d2f85feb6c39",
"metadata": {
"id": "3ab6a724-3d1e-478b-a9e9-d2f85feb6c39"
},
"outputs": [],
"source": [
"from datasets import Audio\n",
"\n",
"raw_datasets = raw_datasets.cast_column(\"audio\", Audio(sampling_rate=16000))"
]
},
{
"cell_type": "markdown",
"id": "161322c2-94f3-4d26-9e1d-d9d5202ca3cf",
"metadata": {
"id": "161322c2-94f3-4d26-9e1d-d9d5202ca3cf"
},
"source": [
"We'll define our pre-processing strategy. We advise that you **do not** lower-case the transcriptions or remove punctuation unless mixing different datasets. This will enable you to fine-tune Whisper models that can predict punctuation and casing. Later, you will see how we can evaluate the predictions without punctuation or casing, so that the models benefit from the WER improvement obtained by normalising the transcriptions while still predicting fully formatted transcriptions."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "d041650e-1c48-4439-87b3-5b6f4a514107",
"metadata": {
"id": "d041650e-1c48-4439-87b3-5b6f4a514107"
},
"outputs": [],
"source": [
"import string\n",
"import re\n",
"\n",
"do_lower_case = True\n",
"do_remove_punctuation = False\n",
"\n",
"punctuation_to_remove = string.punctuation.replace(\"'\", \"\") # don't remove apostrophes\n",
"punctuation_to_remove_regex = f\"[{''.join(punctuation_to_remove)}]\"\n",
"\n",
"if do_remove_punctuation:\n",
" print(\"Removing punctuation: \", punctuation_to_remove)"
]
},
{
"cell_type": "markdown",
"id": "bfaa935b-a11d-497c-88c1-0c4d1bb3247b",
"metadata": {
"id": "bfaa935b-a11d-497c-88c1-0c4d1bb3247b"
},
"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, 🤗 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 perform any optional pre-processing (lower-case or remove punctuation).\n",
"4. We encode the transcriptions to label ids through the use of the tokenizer."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "c085911c-a10a-41ef-8874-306e0503e9bb",
"metadata": {
"id": "c085911c-a10a-41ef-8874-306e0503e9bb"
},
"outputs": [],
"source": [
"def prepare_dataset(batch):\n",
" # load and (possibly) resample audio datato 16kHz\n",
" audio = batch[\"audio\"]\n",
"\n",
" # compute log-Mel input features from input audio array \n",
" batch[\"input_features\"] = processor.feature_extractor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_features[0]\n",
" # compute input length of audio sample in seconds\n",
" batch[\"input_length\"] = len(audio[\"array\"]) / audio[\"sampling_rate\"]\n",
" \n",
" # optional pre-processing steps\n",
" transcription = batch[\"sentence\"]\n",
" #transcription = batch[\"transcription\"]\n",
" if do_lower_case:\n",
" transcription = transcription.lower()\n",
" if do_remove_punctuation:\n",
" transcription = re.sub(punctuation_to_remove_regex, \" \", transcription).strip()\n",
" \n",
" # encode target text to label ids\n",
" batch[\"labels\"] = processor.tokenizer(transcription).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 🤗 Datasets' `.map` method. We'll remove all of the columns from the raw training data, leaving just the `input_features` and `labels` defined in the `prepare_dataset` function:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "a37a7cdb-9013-427f-8de9-6a8d0e9dc684",
"metadata": {
"id": "a37a7cdb-9013-427f-8de9-6a8d0e9dc684"
},
"outputs": [],
"source": [
"vectorized_datasets = raw_datasets.map(prepare_dataset, remove_columns=list(next(iter(raw_datasets.values())).features)).with_format(\"torch\")"
]
},
{
"cell_type": "markdown",
"id": "3d59b37e-4950-47ec-9e3e-2cf2ec7fc750",
"metadata": {
"id": "3d59b37e-4950-47ec-9e3e-2cf2ec7fc750"
},
"source": [
"We can now define how we shuffle the data in the train split. The size of the subset we load is set by the variable `buffer_size`. You can increase or decrease this depending on your memory constraints. In this example, the `buffer_size` is set to 500, meaning 500 samples are loaded before shuffling across the subset. The larger we set this value, the closer to True offline shuffling. The `seed` is set for reproducibility:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "1b145699-acfc-4b1d-93a2-a2ad3d62674c",
"metadata": {
"id": "1b145699-acfc-4b1d-93a2-a2ad3d62674c"
},
"outputs": [],
"source": [
"vectorized_datasets[\"train\"] = vectorized_datasets[\"train\"].shuffle(\n",
" buffer_size=500,\n",
" seed=0,\n",
")"
]
},
{
"cell_type": "markdown",
"id": "666b9ef0-7909-4e1e-a419-87604d233e29",
"metadata": {
"id": "666b9ef0-7909-4e1e-a419-87604d233e29"
},
"source": [
"Finally, we filter any training data with audio samples longer than 30s. These samples would otherwise be truncated by the Whisper feature-extractor which could affect the stability of training. We define a function that returns `True` for samples that are less than 30s, and `False` for those that are longer:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "01cb25ef-4bb0-4325-9461-f59198acadf6",
"metadata": {
"id": "01cb25ef-4bb0-4325-9461-f59198acadf6"
},
"outputs": [],
"source": [
"max_input_length = 30.0\n",
"\n",
"def is_audio_in_length_range(length):\n",
" return length < max_input_length"
]
},
{
"cell_type": "markdown",
"id": "28e37ac3-b1c5-465b-8586-7cfd8d76b0f1",
"metadata": {
"id": "28e37ac3-b1c5-465b-8586-7cfd8d76b0f1"
},
"source": [
"We apply our filter function to all samples of our training dataset through 🤗 Datasets' `.filter` method:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "333f7f6e-6053-4d3b-8924-c733c79b82ac",
"metadata": {
"id": "333f7f6e-6053-4d3b-8924-c733c79b82ac"
},
"outputs": [],
"source": [
"vectorized_datasets[\"train\"] = vectorized_datasets[\"train\"].filter(\n",
" is_audio_in_length_range,\n",
" input_columns=[\"input_length\"],\n",
")"
]
},
{
"cell_type": "markdown",
"id": "263a5a58-0239-4a25-b0df-c625fc9c5810",
"metadata": {
"id": "263a5a58-0239-4a25-b0df-c625fc9c5810"
},
"source": [
"## Training and Evaluation"
]
},
{
"cell_type": "markdown",
"id": "a693e768-c5a6-453f-89a1-b601dcf7daf7",
"metadata": {
"id": "a693e768-c5a6-453f-89a1-b601dcf7daf7"
},
"source": [
"Now that we've prepared our data, we're ready to dive into the training pipeline. \n",
"The [🤗 Trainer](https://huggingface.co/transformers/master/main_classes/trainer.html?highlight=trainer)\n",
"will do much of the heavy lifting for us. All we have to do is:\n",
"\n",
"- Define a data collator: the data collator takes our pre-processed data and prepares PyTorch tensors ready for the model.\n",
"\n",
"- Evaluation metrics: during evaluation, we want to evaluate the model using the [word error rate (WER)](https://huggingface.co/metrics/wer) metric. We need to define a `compute_metrics` function that handles this computation.\n",
"\n",
"- Load a pre-trained checkpoint: we need to load a pre-trained checkpoint and configure it correctly for training.\n",
"\n",
"- Define the training configuration: this will be used by the 🤗 Trainer to define the training schedule."
]
},
{
"cell_type": "markdown",
"id": "8d230e6d-624c-400a-bbf5-fa660881df25",
"metadata": {
"id": "8d230e6d-624c-400a-bbf5-fa660881df25"
},
"source": [
"### Define a Data Collator"
]
},
{
"cell_type": "markdown",
"id": "04def221-0637-4a69-b242-d3f0c1d0ee78",
"metadata": {
"id": "04def221-0637-4a69-b242-d3f0c1d0ee78"
},
"source": [
"The data collator for a sequence-to-sequence speech model is unique in the sense that it \n",
"treats the `input_features` and `labels` independently: the `input_features` must be \n",
"handled by the feature extractor and the `labels` by the tokenizer.\n",
"\n",
"The `input_features` are already padded to 30s and converted to a log-Mel spectrogram \n",
"of fixed dimension by action of the feature extractor, so all we have to do is convert the `input_features`\n",
"to batched PyTorch tensors. We do this using the feature extractor's `.pad` method with `return_tensors=pt`.\n",
"\n",
"The `labels` on the other hand are un-padded. We first pad the sequences\n",
"to the maximum length in the batch using the tokenizer's `.pad` method. The padding tokens \n",
"are then replaced by `-100` so that these tokens are **not** taken into account when \n",
"computing the loss. We then cut the BOS token from the start of the label sequence as we \n",
"append it later during training.\n",
"\n",
"We can leverage the `WhisperProcessor` we defined earlier to perform both the \n",
"feature extractor and the tokenizer operations:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"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",
"@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": 21,
"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 🤗 Evaluate:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "b22b4011-f31f-4b57-b684-c52332f92890",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
"referenced_widgets": [
"763a13d8684c477da8fe6f3bc5d6f8a0",
"af18d0ee0270420f98b40effce8c00c3",
"e09543b4a2cc487a97c3e4388481784f",
"fa0f9ecbfc9d4facacd3fb0a000904f2",
"70204cb91a0c4a4cbd24a56933f4aa72",
"a693eb68b48a49f9b3a44f2fb741d024",
"fbbf0b2e108747b4a19a3101e83e96cf",
"ac6dc9da692c47afbd94f2d7900f4fa8",
"630e2d7f275e4c4890ff1616c0451621",
"7faee8a8d650406d93ffefe8613ee42d",
"bde3af431a5144ba84225fd6bad9b27a"
]
},
"id": "b22b4011-f31f-4b57-b684-c52332f92890",
"outputId": "82a1180e-1d02-4a0c-ecc7-899f445312c0"
},
"outputs": [],
"source": [
"import evaluate\n",
"\n",
"metric = evaluate.load(\"wer\")"
]
},
{
"cell_type": "markdown",
"id": "509f96d7-3f11-4f37-add9-f74a0c44f3fc",
"metadata": {
"id": "509f96d7-3f11-4f37-add9-f74a0c44f3fc"
},
"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. \n",
"Here, we have the option of evaluating with the 'normalised' transcriptions \n",
"and predictions. We recommend you set this to `True` to benefit from the WER \n",
"improvement obtained by normalising the transcriptions."
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "a11d1bfc-9e28-460f-a287-72d8f7bc1acb",
"metadata": {
"id": "a11d1bfc-9e28-460f-a287-72d8f7bc1acb"
},
"outputs": [],
"source": [
"# evaluate with the 'normalised' WER\n",
"do_normalize_eval = True\n",
"\n",
"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] = processor.tokenizer.pad_token_id\n",
"\n",
" # we do not want to group tokens when computing the metrics\n",
" pred_str = processor.tokenizer.batch_decode(pred_ids, skip_special_tokens=True, normalize=do_normalize_eval)\n",
" label_str = processor.tokenizer.batch_decode(label_ids, skip_special_tokens=True, normalize=do_normalize_eval)\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": [
"### Load 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 🤗 Transformers!"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "5a10cc4b-07ec-4ebd-ac1d-7c601023594f",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 81,
"referenced_widgets": [
"f5e0f88d26b945bea293b9e395ba225a",
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"b81d7f526ee74297a80e8fe33d2106b0",
"3ed5de0ab0f846d69ac3fd17b783371e",
"4f7e1454bfcb4d74bb05a556a13071d8",
"23c4f25ebdc54c2daa36cded24c02dc6",
"2fb2b151ebd54ae0afc4423ebcc7a188",
"1a0e147cb34a43b188cbfdf6956dda54",
"da008045bf244c88b6e0c01af180939e",
"a58cf16ffd66453e900bb97e902df0b4",
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"007a9bff95c64a3689f1162efc8bdb65",
"364adfd4a24f43f2b7c65c21928b4d1d",
"d34d5eaf6bea44bbb5e9832c3d2b4c32",
"0a9b7eccba9a47cca67ec37eb1b79c1b",
"30a74dda6a7f4b2e8e3d508f27680a1e",
"7c3248075c0840b1a3e9f0afa9f46824",
"67b5c4edf64d443ba53f6f4fbde22880",
"7a39271725614a07a7f8e72bab6f0e51",
"41b629d254014220bc76a60ffdc62b44"
]
},
"id": "5a10cc4b-07ec-4ebd-ac1d-7c601023594f",
"outputId": "b6c255be-8f5e-4045-b9e0-09b8196263d1"
},
"outputs": [],
"source": [
"from transformers import WhisperForConditionalGeneration\n",
"\n",
"model = WhisperForConditionalGeneration.from_pretrained(\"emilios/whisper-medium-el\")\n",
"#model = WhisperForConditionalGeneration.from_pretrained(\"farsipal/whisper-small-el\")\n",
"\n",
"#model=torch.compile(model0)"
]
},
{
"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)). Set `use_cache` to False since we're using gradient checkpointing, and the two are incompatible:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"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 = []\n",
"model.config.use_cache = False"
]
},
{
"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. Here, you can set the `max_steps` to train for longer. 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": 45,
"id": "0ae3e9af-97b7-4aa0-ae85-20b23b5bcb3a",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "0ae3e9af-97b7-4aa0-ae85-20b23b5bcb3a",
"outputId": "7290f729-fb46-4190-dcc5-5cf01e1c9808"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"PyTorch: setting up devices\n"
]
}
],
"source": [
"from transformers import Seq2SeqTrainingArguments\n",
"\n",
"training_args = Seq2SeqTrainingArguments(\n",
" output_dir=\"./whisper-medium-el\", # your repo name\n",
" #output_dir=\"./whisper-small-el\", # your repo name\n",
" per_device_train_batch_size=32,\n",
" gradient_accumulation_steps=2, # increase by 2x for every 2x decrease in batch size\n",
" learning_rate=1e-5,\n",
" warmup_steps=500,\n",
" max_steps=5000,\n",
" gradient_checkpointing=True,\n",
" fp16=True,\n",
" evaluation_strategy=\"steps\",\n",
" per_device_eval_batch_size=8,\n",
" predict_with_generate=True,\n",
" generation_max_length=225,\n",
" save_steps=1000,\n",
" eval_steps=1000,\n",
" logging_steps=25,\n",
" report_to=[\"tensorboard\"],\n",
" load_best_model_at_end=True,\n",
" metric_for_best_model=\"wer\",\n",
" greater_is_better=False,\n",
" push_to_hub=True,\n",
" #optim=\"adamw_bnb_8bit\"\n",
")\n"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "o72eOpGzD_sK",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "o72eOpGzD_sK",
"outputId": "f26c4279-5dc0-4daa-ffde-b53e8ddd237b"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Thu Dec 8 18:57:05 2022 \n",
"+-----------------------------------------------------------------------------+\n",
"| NVIDIA-SMI 515.65.01 Driver Version: 515.65.01 CUDA Version: 11.7 |\n",
"|-------------------------------+----------------------+----------------------+\n",
"| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
"| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n",
"| | | MIG M. |\n",
"|===============================+======================+======================|\n",
"| 0 NVIDIA A100-SXM... On | 00000000:06:00.0 Off | 0 |\n",
"| N/A 32C P0 55W / 400W | 39261MiB / 40960MiB | 0% Default |\n",
"| | | Disabled |\n",
"+-------------------------------+----------------------+----------------------+\n",
" \n",
"+-----------------------------------------------------------------------------+\n",
"| Processes: |\n",
"| GPU GI CI PID Type Process name GPU Memory |\n",
"| ID ID Usage |\n",
"|=============================================================================|\n",
"| 0 N/A N/A 130327 C /usr/bin/python3 39259MiB |\n",
"+-----------------------------------------------------------------------------+\n"
]
}
],
"source": [
"!nvidia-smi"
]
},
{
"cell_type": "markdown",
"id": "b3a944d8-3112-4552-82a0-be25988b3857",
"metadata": {
"id": "b3a944d8-3112-4552-82a0-be25988b3857"
},
"source": [
"**Note**: if one does not want to upload the model checkpoints to the Hub, \n",
"set `push_to_hub=False`."
]
},
{
"cell_type": "markdown",
"id": "393c883e-3e50-492c-bd58-f51dbf15ee56",
"metadata": {
"id": "393c883e-3e50-492c-bd58-f51dbf15ee56"
},
"source": [
"We then define a custom [Callback](https://huggingface.co/docs/transformers/main_classes/callback) that is called by the 🤗 Trainer on the end of each epoch. The Callback reinitialises and reshuffles the streaming dataset at the beginning of each new epoch - this gives different shuffling across our subsets for every epoch."
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "3ac16b62-b3c0-4c68-8f3d-9ecf471534b2",
"metadata": {
"id": "3ac16b62-b3c0-4c68-8f3d-9ecf471534b2"
},
"outputs": [],
"source": [
"from transformers import TrainerCallback\n",
"from transformers.trainer_pt_utils import IterableDatasetShard\n",
"from torch.utils.data import IterableDataset\n",
"\n",
"# trainer callback to reinitialise and reshuffle the streamable datasets at the beginning of each epoch\n",
"class ShuffleCallback(TrainerCallback):\n",
" def on_epoch_begin(self, args, state, control, train_dataloader, **kwargs):\n",
" if isinstance(train_dataloader.dataset, IterableDatasetShard):\n",
" pass # set_epoch() is handled by the Trainer\n",
" elif isinstance(train_dataloader.dataset, IterableDataset):\n",
" train_dataloader.dataset.set_epoch(train_dataloader.dataset._epoch + 1)"
]
},
{
"cell_type": "markdown",
"id": "bac29114-d226-4f54-97cf-8718c9f94e1e",
"metadata": {
"id": "bac29114-d226-4f54-97cf-8718c9f94e1e"
},
"source": [
"We can forward the training arguments to the 🤗 Trainer along with our model,\n",
"dataset, data collator, `compute_metrics` function and custom callback:"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "d546d7fe-0543-479a-b708-2ebabec19493",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000,
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"text": [
"/home/ubuntu/./whisper-medium-el is already a clone of https://huggingface.co/emilios/whisper-medium-el. Make sure you pull the latest changes with `repo.git_pull()`.\n",
"max_steps is given, it will override any value given in num_train_epochs\n",
"Using cuda_amp half precision backend\n"
]
}
],
"source": [
"from transformers import Seq2SeqTrainer\n",
"\n",
"#import torch._dynamo as dynamo\n",
"#from dynamo import optimize\n",
"\n",
"trainer = Seq2SeqTrainer(\n",
" args=training_args,\n",
" model=model,\n",
" train_dataset=vectorized_datasets[\"train\"],\n",
" eval_dataset=vectorized_datasets[\"test\"],\n",
" data_collator=data_collator,\n",
" compute_metrics=compute_metrics,\n",
" tokenizer=processor,\n",
" callbacks=[ShuffleCallback()],\n",
")"
]
},
{
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"id": "67ab88c3-7091-4e51-8ad5-f5cacbe18449",
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"source": [
"We'll save the model and processor to the output directory before training:"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "a1ccb9ed-cbc8-4419-91c0-651e9424b672",
"metadata": {
"id": "a1ccb9ed-cbc8-4419-91c0-651e9424b672"
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"text": [
"Configuration saved in ./whisper-medium-el/config.json\n",
"Model weights saved in ./whisper-medium-el/pytorch_model.bin\n",
"Feature extractor saved in ./whisper-medium-el/preprocessor_config.json\n",
"tokenizer config file saved in ./whisper-medium-el/tokenizer_config.json\n",
"Special tokens file saved in ./whisper-medium-el/special_tokens_map.json\n",
"added tokens file saved in ./whisper-medium-el/added_tokens.json\n"
]
}
],
"source": [
"model.save_pretrained(training_args.output_dir)\n",
"processor.save_pretrained(training_args.output_dir)"
]
},
{
"cell_type": "markdown",
"id": "7f404cf9-4345-468c-8196-4bd101d9bd51",
"metadata": {
"id": "7f404cf9-4345-468c-8196-4bd101d9bd51"
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"source": [
"### Training"
]
},
{
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"id": "994fcdfa-6074-4c04-96c2-ed8781c9dbc4",
"metadata": {
"id": "994fcdfa-6074-4c04-96c2-ed8781c9dbc4"
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"source": [
"Training will take approximately 5-10 hours depending on the GPU\n",
"allocated to this Google Colab. If using this Google Colab directly to \n",
"fine-tune a Whisper model, you should make sure that training isn't \n",
"interrupted due to inactivity. A simple workaround to prevent this is \n",
"to paste the following code into the console of this tab (_right mouse click_ \n",
"-> _inspect_ -> _Console tab_ -> _insert code_)."
]
},
{
"cell_type": "markdown",
"id": "ec886310-c18d-4a8d-9d6a-842a11b22d6a",
"metadata": {
"id": "ec886310-c18d-4a8d-9d6a-842a11b22d6a"
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"source": [
"```javascript\n",
"function ConnectButton(){\n",
" console.log(\"Connect pushed\"); \n",
" document.querySelector(\"#top-toolbar > colab-connect-button\").shadowRoot.querySelector(\"#connect\").click() \n",
"}\n",
"setInterval(ConnectButton, 60000);\n",
"```"
]
},
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"source": [
"The peak GPU memory for the given training configuration is approximately 36GB. \n",
"Depending on your GPU, it is possible that you will encounter a CUDA `\"out-of-memory\"` error when you launch training. \n",
"In this case, you can reduce the `per_device_train_batch_size` incrementally by factors of 2 \n",
"and employ [`gradient_accumulation_steps`](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.Seq2SeqTrainingArguments.gradient_accumulation_steps)\n",
"to compensate.\n",
"\n",
"To launch training, simply execute:"
]
},
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"execution_count": null,
"id": "ee8b7b8e-1c9a-4d77-9137-1778a629e6de",
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"text": [
"/home/ubuntu/.local/lib/python3.8/site-packages/transformers/optimization.py:306: 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 = 320000\n",
" Num Epochs = 9223372036854775807\n",
" Instantaneous batch size per device = 32\n",
" Total train batch size (w. parallel, distributed & accumulation) = 64\n",
" Gradient Accumulation steps = 2\n",
" Total optimization steps = 5000\n",
" Number of trainable parameters = 763857920\n",
"Reading metadata...: 1914it [00:00, 13829.19it/s]\n"
]
}
],
"source": [
"trainer.train()"
]
},
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"id": "747c6a6e",
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"source": [
"(note that training may take some time to commence as we load the first training data samples with streaming mode)"
]
},
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"cell_type": "markdown",
"id": "810ced54-7187-4a06-b2fe-ba6dcca94dc3",
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"source": [
"We can label our checkpoint with the `whisper-event` tag on push by setting the appropriate key-word arguments (kwargs):"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6dd0e310-9b07-4133-ac14-2ed2d7524e22",
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"kwargs = {\n",
" \"dataset_tags\": \"mozilla-foundation/common_voice_11_0\",\n",
" #\"dataset_tags\": \"google/fleurs\",\n",
" \"dataset\": \"Common Voice 11.0\", # a 'pretty' name for the training dataset\n",
" #\"dataset\": \"Google FLEURS\", # a 'pretty' name for the training dataset\n",
" \"language\": \"el\",\n",
" \"model_name\": \"Whisper Medium El - Greek One\", # a 'pretty' name for your model\n",
" \"finetuned_from\": \"openai/whisper-medium\",\n",
" \"tasks\": \"automatic-speech-recognition\",\n",
" \"tags\": \"hf-asr-leaderboard, whisper-medium, mozilla-foundation/common_voice_11_0, greek, whisper-event\",\n",
"}"
]
},
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"id": "090d676a-f944-4297-a938-a40eda0b2b68",
"metadata": {
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},
"source": [
"The training results can now be uploaded to the Hub. To do so, execute the `push_to_hub` command:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "95737cda-c5dd-4887-a4d0-dfcb0d61d977",
"metadata": {
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},
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]
}
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