diff --git "a/YA_rab_al_alamin.ipynb" "b/YA_rab_al_alamin.ipynb" deleted file mode 100644--- "a/YA_rab_al_alamin.ipynb" +++ /dev/null @@ -1,16332 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "5cefac89" - }, - "source": [ - "# Fine-tune Whisper (large) with LoRA & BNB powerd by PEFT ⚡️" - ], - "id": "5cefac89" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "090fa3ed" - }, - "source": [ - "A one size fits all notebook, to fine-tune Whisper (large) on a consumer GPU with less than 8GB GPU VRAM, all with comparable performance to full-finetuning. ⚡️\n", - "\n", - "We present a step-by-step guide on how to fine-tune Whisper with Common Voice 13.0 dataset using 🤗 Transformers and PEFT. In this Colab, we leverage `PEFT` and `bitsandbytes` to train a `whisper-large-v2` checkpoint seamlessly with a free T4 GPU (16 GB VRAM).\n", - "\n", - "For more details on Whisper fine-tuning, datasets and metrics, refer to Sanchit Gandhi's brilliant blogpost: [Fine-Tune Whisper For Multilingual ASR with 🤗 Transformers](https://huggingface.co/blog/fine-tune-whisper)\n", - "\n", - "## Why Parameter Efficient Fine Tuning ([PEFT](https://github.com/huggingface/peft))?\n", - "\n", - "As the model size continue to increase, fine tuning a model has become both computationally expensive and storage heavy. For example, a `Whisper-large-v2` model requires ~24GB of GPU VRAM to fine-tune for full fine-tuning and requires ~7 GB of storage for each fine-tuned storage. For low-resource environments this becomes quite a bottleneck and often near impossible to get meaningful results.\n", - "\n", - "Cue, PEFT, with PEFT you can tackle this bottleneck head on. PEFT approaches (like Low Rank Adaptation) only fine-tune a small number of (extra) model parameters while freezing most parameters of the pretrained model, thereby greatly decreasing the computational and storage costs. We've observed that it also overcomes the issues of catastrophic forgetting, a behaviour observed during the full finetuning of large models.\n", - "\n", - "### Aha! So wait, what's this LoRA thing?\n", - "\n", - "PEFT comes out-of-the-box with multiple parameter efficient techniques. One such technique is [Low Rank Adaptation or LoRA](https://github.com/microsoft/LoRA). LoRA freezes the pre-trained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture. This greatly reduces the number of trainable parameters for downstream tasks.\n", - "\n", - "LoRA performs on-par or better than fine-tuning in model quality despite having fewer trainable parameters, a higher training throughput, and, unlike adapters, no additional inference latency.\n", - "\n", - "### That's all cool, but show me the numbers?\n", - "\n", - "Don't worry, we got ya! We ran multiple experiments to compare a full fine-tuning of Whisper-large-v2 checkpoint and that with PEFT, here's what we found:\n", - "\n", - "1. We were able to fine-tune a 1.6B parameter model with less than 8GB GPU VRAM. 🤯\n", - "2. With significantly less number of traininable parameters, we were able to fit almost **5x** more batch size. 📈\n", - "3. The resultant checkpoint were less than 1% the size of the original model, ~60MB (i.e. 1% the size of orignal model) 🚀\n", - "\n", - "To make things even better, all of this comes with minimal changes to the existing 🤗 transformers Whisper inference codebase.\n", - "\n", - "### Curious to test this out for yourself? Follow along!" - ], - "id": "090fa3ed" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "625e47a0" - }, - "source": [ - "## Prepare Environment\n", - "\n", - "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 `librosa` package to pre-process audio files, `evaluate` and `jiwer` to\n", - "assess the performance of our model. Finally, we'll\n", - "use `PEFT`, `bitsandbytes`, `accelerate` to prepare and fine-tune the model with LoRA." - ], - "id": "625e47a0" - }, - { - "cell_type": "code", - "source": [ - "!add-apt-repository -y ppa:jonathonf/ffmpeg-4\n", - "!apt update\n", - "!apt install -y ffmpeg" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "RLNCEM9IPH-q", - "outputId": "fdbb9296-33e0-4939-a9c2-fb9dd686021b", - "collapsed": true - }, - "id": "RLNCEM9IPH-q", - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Repository: 'deb https://ppa.launchpadcontent.net/jonathonf/ffmpeg-4/ubuntu/ jammy main'\n", - "Description:\n", - "Backport of FFmpeg 4 and associated libraries. Now includes AOM/AV1 support!\n", - "\n", - "FDK AAC is not compatible with GPL and FFmpeg can't be redistributed with it included. Please don't ask for it to be added to this public PPA.\n", - "\n", - "---\n", - "\n", - "PPA supporters:\n", - "\n", - "BigBlueButton (https://bigbluebutton.org)\n", - "\n", - "---\n", - "\n", - "Donate to FFMPEG: https://ffmpeg.org/donations.html\n", - "Donate to Debian: https://www.debian.org/donations\n", - "Donate to this PPA: https://ko-fi.com/jonathonf\n", - "More info: https://launchpad.net/~jonathonf/+archive/ubuntu/ffmpeg-4\n", - "Adding repository.\n", - "Adding deb entry to /etc/apt/sources.list.d/jonathonf-ubuntu-ffmpeg-4-jammy.list\n", - "Adding disabled deb-src entry to /etc/apt/sources.list.d/jonathonf-ubuntu-ffmpeg-4-jammy.list\n", - "Adding key to /etc/apt/trusted.gpg.d/jonathonf-ubuntu-ffmpeg-4.gpg with fingerprint 4AB0F789CBA31744CC7DA76A8CF63AD3F06FC659\n", - "Get:1 https://cloud.r-project.org/bin/linux/ubuntu jammy-cran40/ InRelease [3,626 B]\n", - "Hit:2 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 InRelease\n", - "Hit:3 https://ppa.launchpadcontent.net/c2d4u.team/c2d4u4.0+/ubuntu jammy InRelease\n", - "Hit:4 https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu jammy InRelease\n", - "Hit:5 https://ppa.launchpadcontent.net/graphics-drivers/ppa/ubuntu jammy InRelease\n", - "Ign:6 https://ppa.launchpadcontent.net/jonathonf/ffmpeg-4/ubuntu jammy InRelease\n", - "Hit:7 https://ppa.launchpadcontent.net/ubuntugis/ppa/ubuntu jammy InRelease\n", - "Err:8 https://ppa.launchpadcontent.net/jonathonf/ffmpeg-4/ubuntu jammy Release\n", - " 404 Not Found [IP: 185.125.190.80 443]\n", - "Get:9 http://security.ubuntu.com/ubuntu jammy-security InRelease [129 kB]\n", - "Hit:10 http://archive.ubuntu.com/ubuntu jammy InRelease\n", - "Get:11 http://security.ubuntu.com/ubuntu jammy-security/universe amd64 Packages [1,090 kB]\n", - "Get:12 http://archive.ubuntu.com/ubuntu jammy-updates InRelease [128 kB]\n", - "Hit:13 http://archive.ubuntu.com/ubuntu jammy-backports InRelease\n", - "Get:14 http://security.ubuntu.com/ubuntu jammy-security/restricted amd64 Packages [2,450 kB]\n", - "Get:15 http://security.ubuntu.com/ubuntu jammy-security/main amd64 Packages [1,897 kB]\n", - "Get:16 http://archive.ubuntu.com/ubuntu jammy-updates/restricted amd64 Packages [2,525 kB]\n", - "Get:17 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 Packages [1,390 kB]\n", - "Get:18 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 Packages [2,172 kB]\n", - "Reading package lists... Done\n", - "E: The repository 'https://ppa.launchpadcontent.net/jonathonf/ffmpeg-4/ubuntu jammy 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", - "Hit:1 https://cloud.r-project.org/bin/linux/ubuntu jammy-cran40/ InRelease\n", - "Hit:2 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 InRelease\n", - "Hit:3 https://ppa.launchpadcontent.net/c2d4u.team/c2d4u4.0+/ubuntu jammy InRelease\n", - "Hit:4 https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu jammy InRelease\n", - "Hit:5 https://ppa.launchpadcontent.net/graphics-drivers/ppa/ubuntu jammy InRelease\n", - "Ign:6 https://ppa.launchpadcontent.net/jonathonf/ffmpeg-4/ubuntu jammy InRelease\n", - "Hit:7 https://ppa.launchpadcontent.net/ubuntugis/ppa/ubuntu jammy InRelease\n", - "Err:8 https://ppa.launchpadcontent.net/jonathonf/ffmpeg-4/ubuntu jammy Release\n", - " 404 Not Found [IP: 185.125.190.80 443]\n", - "Hit:9 http://archive.ubuntu.com/ubuntu jammy InRelease\n", - "Hit:10 http://archive.ubuntu.com/ubuntu jammy-updates InRelease\n", - "Hit:11 http://security.ubuntu.com/ubuntu jammy-security InRelease\n", - "Hit:12 http://archive.ubuntu.com/ubuntu jammy-backports InRelease\n", - "Reading package lists... Done\n", - "\u001b[1;31mE: \u001b[0mThe repository 'https://ppa.launchpadcontent.net/jonathonf/ffmpeg-4/ubuntu jammy 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... Done\n", - "Reading state information... Done\n", - "ffmpeg is already the newest version (7:4.4.2-0ubuntu0.22.04.1).\n", - "0 upgraded, 0 newly installed, 0 to remove and 52 not upgraded.\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "!pip install datasets>=2.6.1\n", - "!pip install git+https://github.com/huggingface/transformers\n", - "!pip install librosa\n", - "!pip install evaluate>=0.30\n", - "!pip install jiwer\n", - "!pip install -q bitsandbytes datasets accelerate loralib\n", - "!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git@main" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "z-rH2K1IL91f", - "outputId": "c0fd63e3-78ce-47ac-f81f-e0ed1235dea1", - "collapsed": true - }, - "id": "z-rH2K1IL91f", - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\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", - "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\u001b[0m\u001b[31m\n", - "\u001b[0mCollecting git+https://github.com/huggingface/transformers\n", - " Cloning https://github.com/huggingface/transformers to /tmp/pip-req-build-oqicd47m\n", - " Running command git clone --filter=blob:none --quiet https://github.com/huggingface/transformers /tmp/pip-req-build-oqicd47m\n", - " Resolved https://github.com/huggingface/transformers to commit 1c77b3d9cf85e8950ade3f48b9998eefc84af514\n", - " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", - " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", - " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", - "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers==4.42.0.dev0) (3.14.0)\n", - "Requirement already satisfied: huggingface-hub<1.0,>=0.23.2 in /usr/local/lib/python3.10/dist-packages (from transformers==4.42.0.dev0) (0.23.2)\n", - 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" Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for transformers: filename=transformers-4.42.0.dev0-py3-none-any.whl size=9156923 sha256=36fe3c97f85e5bab2a48e74a89bf75d4c7eb39539d7791972d0fa55d087aeea7\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-4coa1hrf/wheels/c0/14/d6/6c9a5582d2ac191ec0a483be151a4495fe1eb2a6706ca49f1b\n", - "Successfully built transformers\n", - "Installing collected packages: transformers\n", - " Attempting uninstall: transformers\n", - " Found existing installation: transformers 4.41.2\n", - " Uninstalling transformers-4.41.2:\n", - " Successfully uninstalled transformers-4.41.2\n", - "Successfully installed transformers-4.42.0.dev0\n", - "Requirement already satisfied: librosa in /usr/local/lib/python3.10/dist-packages (0.10.2.post1)\n", - "Requirement already satisfied: audioread>=2.1.9 in /usr/local/lib/python3.10/dist-packages (from librosa) (3.0.1)\n", - "Requirement already satisfied: numpy!=1.22.0,!=1.22.1,!=1.22.2,>=1.20.3 in /usr/local/lib/python3.10/dist-packages (from librosa) (1.25.2)\n", - 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" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", - " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", - " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", - " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", - " Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "!add-apt-repository -y ppa:jonathonf/ffmpeg-4\n", - "!apt update\n", - "!apt install -y ffmpeg" - ], - "metadata": { - "id": "66iVrvlALjZS", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "collapsed": true, - "outputId": "3544c6d9-235e-4059-a91b-f1749cd5c402" - }, - "id": "66iVrvlALjZS", - "execution_count": 3, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Repository: 'deb https://ppa.launchpadcontent.net/jonathonf/ffmpeg-4/ubuntu/ jammy main'\n", - "Description:\n", - "Backport of FFmpeg 4 and associated libraries. Now includes AOM/AV1 support!\n", - "\n", - "FDK AAC is not compatible with GPL and FFmpeg can't be redistributed with it included. Please don't ask for it to be added to this public PPA.\n", - "\n", - "---\n", - "\n", - "PPA supporters:\n", - "\n", - "BigBlueButton (https://bigbluebutton.org)\n", - "\n", - "---\n", - "\n", - "Donate to FFMPEG: https://ffmpeg.org/donations.html\n", - "Donate to Debian: https://www.debian.org/donations\n", - "Donate to this PPA: https://ko-fi.com/jonathonf\n", - "More info: https://launchpad.net/~jonathonf/+archive/ubuntu/ffmpeg-4\n", - "Adding repository.\n", - "Found existing deb entry in /etc/apt/sources.list.d/jonathonf-ubuntu-ffmpeg-4-jammy.list\n", - "Adding deb entry to /etc/apt/sources.list.d/jonathonf-ubuntu-ffmpeg-4-jammy.list\n", - "Found existing deb-src entry in /etc/apt/sources.list.d/jonathonf-ubuntu-ffmpeg-4-jammy.list\n", - "Adding disabled deb-src entry to /etc/apt/sources.list.d/jonathonf-ubuntu-ffmpeg-4-jammy.list\n", - "Adding key to /etc/apt/trusted.gpg.d/jonathonf-ubuntu-ffmpeg-4.gpg with fingerprint 4AB0F789CBA31744CC7DA76A8CF63AD3F06FC659\n", - "Hit:1 https://cloud.r-project.org/bin/linux/ubuntu jammy-cran40/ InRelease\n", - "Hit:2 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 InRelease\n", - "Hit:3 https://ppa.launchpadcontent.net/c2d4u.team/c2d4u4.0+/ubuntu jammy InRelease\n", - "Hit:4 https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu jammy InRelease\n", - "Hit:5 https://ppa.launchpadcontent.net/graphics-drivers/ppa/ubuntu jammy InRelease\n", - "Ign:6 https://ppa.launchpadcontent.net/jonathonf/ffmpeg-4/ubuntu jammy InRelease\n", - "Hit:7 https://ppa.launchpadcontent.net/ubuntugis/ppa/ubuntu jammy InRelease\n", - "Err:8 https://ppa.launchpadcontent.net/jonathonf/ffmpeg-4/ubuntu jammy Release\n", - " 404 Not Found [IP: 185.125.190.80 443]\n", - "Hit:9 http://security.ubuntu.com/ubuntu jammy-security InRelease\n", - "Hit:10 http://archive.ubuntu.com/ubuntu jammy InRelease\n", - "Hit:11 http://archive.ubuntu.com/ubuntu jammy-updates InRelease\n", - "Hit:12 http://archive.ubuntu.com/ubuntu jammy-backports InRelease\n", - "Reading package lists... Done\n", - "E: The repository 'https://ppa.launchpadcontent.net/jonathonf/ffmpeg-4/ubuntu jammy 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", - "Hit:1 https://cloud.r-project.org/bin/linux/ubuntu jammy-cran40/ InRelease\n", - "Hit:2 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 InRelease\n", - "Hit:3 https://ppa.launchpadcontent.net/c2d4u.team/c2d4u4.0+/ubuntu jammy InRelease\n", - "Hit:4 https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu jammy InRelease\n", - "Hit:5 https://ppa.launchpadcontent.net/graphics-drivers/ppa/ubuntu jammy InRelease\n", - "Ign:6 https://ppa.launchpadcontent.net/jonathonf/ffmpeg-4/ubuntu jammy InRelease\n", - "Hit:7 https://ppa.launchpadcontent.net/ubuntugis/ppa/ubuntu jammy InRelease\n", - "Err:8 https://ppa.launchpadcontent.net/jonathonf/ffmpeg-4/ubuntu jammy Release\n", - " 404 Not Found [IP: 185.125.190.80 443]\n", - "Hit:9 http://archive.ubuntu.com/ubuntu jammy InRelease\n", - "Hit:10 http://archive.ubuntu.com/ubuntu jammy-updates InRelease\n", - "Hit:11 http://archive.ubuntu.com/ubuntu jammy-backports InRelease\n", - "Hit:12 http://security.ubuntu.com/ubuntu jammy-security InRelease\n", - "Reading package lists... Done\n", - "\u001b[1;31mE: \u001b[0mThe repository 'https://ppa.launchpadcontent.net/jonathonf/ffmpeg-4/ubuntu jammy 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... Done\n", - "Reading state information... Done\n", - "ffmpeg is already the newest version (7:4.4.2-0ubuntu0.22.04.1).\n", - "0 upgraded, 0 newly installed, 0 to remove and 52 not upgraded.\n" - ] - } - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "r_Ivl7qlX0dz" - }, - "outputs": [], - "source": [], - "id": "r_Ivl7qlX0dz" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "2MvOaXjUjK71" - }, - "source": [ - "With the environment now set up, 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 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_.\n", - "\n", - "We can verify that we've been assigned a GPU and view its specifications:" - ], - "id": "2MvOaXjUjK71" - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "2kBtM9XSjKE5", - "outputId": "b137c985-f464-46cf-8fde-6aaab36a8394" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Wed Jun 12 13:49:01 2024 \n", - "+---------------------------------------------------------------------------------------+\n", - "| NVIDIA-SMI 535.104.05 Driver Version: 535.104.05 CUDA Version: 12.2 |\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 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n", - "| N/A 36C P8 9W / 70W | 0MiB / 15360MiB | 0% Default |\n", - "| | | N/A |\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)" - ], - "id": "2kBtM9XSjKE5" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "6WwnavbBuezQ" - }, - "source": [ - "Alrighty! Let's configure our environment to ensure it uses the GPU provided by Colab to us." - ], - "id": "6WwnavbBuezQ" - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "id": "e1da5fff" - }, - "outputs": [], - "source": [ - "import os\n", - "\n", - "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"" - ], - "id": "e1da5fff" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "8a528c1a" - }, - "source": [ - "We strongly advise you to upload model checkpoints directly the [Hugging Face Hub](https://huggingface.co/)\n", - "whilst training. The Hub provides:\n", - "- Integrated version control: you can be sure that no model checkpoint is lost during training.\n", - "- Tensorboard logs: track important metrics over the course of training.\n", - "- Model cards: document what a model does and its intended use cases.\n", - "- Community: an easy way to share and collaborate with the community!\n", - "\n", - "Linking the notebook to the Hub is straightforward - it simply requires entering your Hub authentication token when prompted. Find your Hub authentication token [here](https://huggingface.co/settings/tokens):" - ], - "id": "8a528c1a" - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 145, - "referenced_widgets": [ - "ea6c4c99c2fb4e03a5b59ad727c6357d", - "56a8f384c0734362be229687ca7aeca7", - "263bce23a7ac4351ba449276bb12826e", - "bee42a2c931c4b16a83243be9a92d1f6", - "3622f8bab3c641aa90ac3b10454b1083", - "12df3fde78254e60a8941d9206ac2492", - "808816ad92ca497fb0fca7879121589b", - "09c9172cff884f3597b2777f6f4dc247", - "95497ee173284fd69f434bfc0ddca453", - "189fafda604e479590b20c5e3ebbe433", - "6f84977d8a4449549842e68a2d6756ac", - "e100e4b0e5344b21a9509bafe534c002", - "a07e23c4bd28447aa94efc4bde1e953d", - "788a7174482f4b7484b6be0d1bd23358", - "3dbddd170d1b4820953eff6f51c64557", - "5c90326961794318b77505ed5bfa802a", - "340a80490be748819faa8fc572811bca", - "5b896fe8a3a34117a7af80fe00f82cc9", - "fe81fe4577244d0c9dfeb0c3099ad9fb", - "3f7d2dc0ccd848b5af096d64f52b580c", - "c044390b5d3b4508bb45ca145ac12d52", - "3f5fdeda50f64b65818c2ebfe061b367", - "371ea5bc7bb14f4199f0831d9737cb58", - "4326dce8c9ad49eaa3aae8a7ba46854f", - "e1f72bf6044645ed9489ffa12dd088d7", - "dfb022a2bb784d20a18f1a3ff6e5fe5f", - "616ad67982604c4783b6d6da7cb66e6f", - "e590f86fcc5f4451a7e793358a888b9e", - "72a74b1fe1e146fdb5c40404c4fb3034", - "2e38b93c15b54f7197f718a985a2abbd", - "fb8c13a9e88e4b34991ac1ea8d8529f0", - "6cd5950437af4fe9a3fa4fea75936903" - ] - }, - "id": "ed0OpduhX2JF", - "outputId": "7977a7be-fddd-496e-9ff0-f7112d3c385e" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "VBox(children=(HTML(value='
' instead.\n", - " warnings.warn(\n", - "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n", - "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", - "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", - "You will be able to reuse this secret in all of your notebooks.\n", - "Please note that authentication is recommended but still optional to access public models or datasets.\n", - " warnings.warn(\n", - "/usr/local/lib/python3.10/dist-packages/datasets/load.py:1491: FutureWarning: The repository for mozilla-foundation/common_voice_16_1 contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/mozilla-foundation/common_voice_16_1\n", - "You can avoid this message in future by passing the argument `trust_remote_code=True`.\n", - "Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.\n", - " warnings.warn(\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "Downloading builder script: 0%| | 0.00/8.17k [00:00' instead.\n", - " warnings.warn(\n", - "/usr/local/lib/python3.10/dist-packages/datasets/load.py:1491: FutureWarning: The repository for mozilla-foundation/common_voice_16_1 contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/mozilla-foundation/common_voice_16_1\n", - "You can avoid this message in future by passing the argument `trust_remote_code=True`.\n", - "Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.\n", - " warnings.warn(\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "DatasetDict({\n", - " train: Dataset({\n", - " features: ['client_id', 'path', 'audio', 'sentence', 'up_votes', 'down_votes', 'age', 'gender', 'accent', 'locale', 'segment', 'variant'],\n", - " num_rows: 2000\n", - " })\n", - " test: Dataset({\n", - " features: ['client_id', 'path', 'audio', 'sentence', 'up_votes', 'down_votes', 'age', 'gender', 'accent', 'locale', 'segment', 'variant'],\n", - " num_rows: 400\n", - " })\n", - "})\n" - ] - } - ], - "source": [ - "from datasets import load_dataset, DatasetDict\n", - "\n", - "common_voice = DatasetDict()\n", - "\n", - "common_voice[\"train\"] = load_dataset(dataset_name, language_abbr, split=\"train[:2000]\", use_auth_token=True)\n", - "common_voice[\"test\"] = load_dataset(dataset_name, language_abbr, split=\"test[:400]\", use_auth_token=True)\n", - "\n", - "print(common_voice)" - ], - "id": "a2787582-554f-44ce-9f38-4180a5ed6b44" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "805b1c56" - }, - "source": [ - "Most ASR datasets only provide input audio samples (`audio`) and the\n", - "corresponding transcribed text (`sentence`). Common Voice contains additional\n", - "metadata information, such as `accent` and `locale`, which we can disregard for ASR.\n", - "Keeping the notebook as general as possible, we only consider the input audio and\n", - "transcribed text for fine-tuning, discarding the additional metadata information:" - ], - "id": "805b1c56" - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "20ba635d-518c-47ac-97ee-3cad25f1e0ce", - "outputId": "31107e80-ced6-486a-efdb-15fe695a41f3" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "DatasetDict({\n", - " train: Dataset({\n", - " features: ['audio', 'sentence'],\n", - " num_rows: 2000\n", - " })\n", - " test: Dataset({\n", - " features: ['audio', 'sentence'],\n", - " num_rows: 400\n", - " })\n", - "})\n" - ] - } - ], - "source": [ - "common_voice = common_voice.remove_columns(\n", - " [\"accent\", \"age\", \"client_id\", \"down_votes\", \"gender\", \"locale\", \"path\", \"segment\", \"up_votes\", \"variant\"]\n", - ")\n", - "\n", - "print(common_voice)" - ], - "id": "20ba635d-518c-47ac-97ee-3cad25f1e0ce" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "2d63b2d2-f68a-4d74-b7f1-5127f6d16605" - }, - "source": [ - "## Prepare Feature Extractor, Tokenizer and Data\n", - "\n", - "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." - ], - "id": "2d63b2d2-f68a-4d74-b7f1-5127f6d16605" - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 49, - "referenced_widgets": [ - "14cbc954087d4ec39f4b08941f5be560", - "313e6062a11d4ec0864c0f959b2eb644", - "91d77b4d7be842a2b2f85244e3ad788f", - "830b74bb1e04437593692c176a293ec6", - "e155030b45c64600b34e88881ecd18c5", - "7e4a0829f4904a579899ec4dee6c0bf0", - "983f140274914da8b03b09036a5672c1", - "99c0ca3df57942e3a160a26bf3cbda7d", - "a065830f366042968677081906670c24", - "fdcea4b7f1f34e13a0921662062d11fa", - "4107fff3ec9a4891b597df313bec4523" - ] - }, - "id": "bc77d7bb-f9e2-47f5-b663-30f7a4321ce5", - "outputId": "2229b44d-6d37-484a-9c37-39b448e25182" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "preprocessor_config.json: 0%| | 0.00/185k [00:00 1 will enable multiprocessing. If the `.map` method hangs with multiprocessing, set `num_proc=1` and process the dataset sequentially.\n", - "\n", - "Make yourself some tea 🍵, depending on dataset size, this might take 20-30 minutes ⏰" - ], - "id": "70b319fb-2439-4ef6-a70d-a47bf41c4a13" - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 81, - "referenced_widgets": [ - "f4980d6fba5e447495e532f5d108d48b", - "87797671706644e0bf5f7207b318d9e9", - "6102e5218401445e8c19d4c19e966c67", - "d8d5352a570b428da102867684f05e87", - "c2f8ce4f5ada427381f7941dd62c1881", - "3a145669e4d840d6b289fcbac6f73b3f", - "fc5d2b937088451f9506053b023c78bc", - "1ec712a337f44acbb3c8a3f35f1125fa", - "b55fe33c47b64978a173944e14cccde2", - "cbad04115f3d433aa18bd15ff607d0f3", - "1ca2ffa9678449f19f45e6b316523dc5", - "c38837131d3b48648b7bd029b00f15ba", - "0c29c7024ad64ceaaeed46f7a19914df", - "2d24d2d9fb7d4388af7d1ebff5c86f66", - "309fec6aace34206a79689bd2b085a61", - "32634e82118e4ba1aeab218b282b3b1c", - "84cfa826feda4296ba9b790f5f6b4c36", - "4b85e9d26e834ddba3f407cf1d1d6019", - "d9f961ef72d743cb84593067a3a66f42", - "16442de3d9684374bf3b04bae661ad94", - "d3eb218824e240dabb6bdb2a5596cd3c", - "2b1018d4be24409db69ebefcef65e4ca" - ] - }, - "id": "7b73ab39-ffaf-4b9e-86e5-782963c6134b", - "outputId": "eac4c9f9-7d9d-4b7b-e8c4-4efcfca18838" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "Map (num_proc=2): 0%| | 0/2000 [00:00 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" - ], - "id": "8326221e-ec13-4731-bb4e-51e5fc1486c5" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3cae7dbf-8a50-456e-a3a8-7fd005390f86" - }, - "source": [ - "Let's initialise the data collator we've just defined:" - ], - "id": "3cae7dbf-8a50-456e-a3a8-7fd005390f86" - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "id": "fc834702-c0d3-4a96-b101-7b87be32bf42" - }, - "outputs": [], - "source": [ - "data_collator = DataCollatorSpeechSeq2SeqWithPadding(processor=processor)" - ], - "id": "fc834702-c0d3-4a96-b101-7b87be32bf42" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "d62bb2ab-750a-45e7-82e9-61d6f4805698" - }, - "source": [ - "### Evaluation Metrics" - ], - "id": "d62bb2ab-750a-45e7-82e9-61d6f4805698" - }, - { - "cell_type": "markdown", - "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:" - ], - "id": "66fee1a7-a44c-461e-b047-c3917221572e" - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "id": "b22b4011-f31f-4b57-b684-c52332f92890" - }, - "outputs": [], - "source": [ - "\n", - "import evaluate\n", - "metric = evaluate.load(\"wer\")" - ], - "id": "b22b4011-f31f-4b57-b684-c52332f92890" - }, - { - "cell_type": "code", - "source": [ - "pip install --upgrade bitsandbytes\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "jAloWte0JC7f", - "outputId": "22ed50f5-e91b-49b6-b9c1-aa0858d62d5f" - }, - "id": "jAloWte0JC7f", - "execution_count": 26, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Requirement already satisfied: bitsandbytes in /usr/local/lib/python3.10/dist-packages (0.43.1)\n", - "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from bitsandbytes) (2.3.0+cu121)\n", - "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from bitsandbytes) (1.25.2)\n", - "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (3.14.0)\n", - "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (4.12.1)\n", - "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (1.12.1)\n", - "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (3.3)\n", - "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (3.1.4)\n", - "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (2023.6.0)\n", - "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (12.1.105)\n", - "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (12.1.105)\n", - "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (12.1.105)\n", - "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (8.9.2.26)\n", - "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (12.1.3.1)\n", - "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (11.0.2.54)\n", - "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (10.3.2.106)\n", - "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (11.4.5.107)\n", - "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (12.1.0.106)\n", - "Requirement already satisfied: nvidia-nccl-cu12==2.20.5 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (2.20.5)\n", - "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (12.1.105)\n", - "Requirement already satisfied: triton==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (2.3.0)\n", - "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch->bitsandbytes) (12.5.40)\n", - "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch->bitsandbytes) (2.1.5)\n", - "Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch->bitsandbytes) (1.3.0)\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "daf2a825-6d9f-4a23-b145-c37c0039075b" - }, - "source": [ - "### Load a Pre-Trained Checkpoint" - ], - "id": "daf2a825-6d9f-4a23-b145-c37c0039075b" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "437a97fa-4864-476b-8abc-f28b8166cfa5" - }, - "source": [ - "Now let's load the pre-trained Whisper checkpoint. Again, this\n", - "is trivial through use of 🤗 Transformers!\n", - "\n", - "To reduce our models memory footprint, we load the model in 8bit, this means we quantize the model to use 1/4th precision (when comapared to float32) with minimal loss to performance. To read more about how this works, head over [here](https://huggingface.co/blog/hf-bitsandbytes-integration)." - ], - "id": "437a97fa-4864-476b-8abc-f28b8166cfa5" - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 131, - "referenced_widgets": [ - "2646865dba7c4522afde562e7e9ad135", - "777d55c4bc284a59bde1c51e55744d4d", - "09aa6553281440b3bd10b6c8fefa197d", - "1b1b7707d2114db79b249f46d362e09c", - "d65b15bca0e643b58a83a07e7fe262d0", - "474c322cd2894c28b67bcdc3bf119ea5", - "755bc4b6c69b4ffc8bb0c09e0d423945", - "24e14070975a4138a7b67fa1aaa7546f", - "6d89b654b20643e0a3f899073492ec9c", - "6a5586af82fb4af0bcdeafed6b2c9533", - "e9b853a619f84a17b6d5e0b2ea661b07", - "13f6308c95ab48dbacedd1a13edea50c", - "b9851afe0aef4e2da39f55dfb1fe7ee3", - "a0d2987bbed9407b967b2025419c0035", - "213bc01b41194ef99470c1aaaa12bbe0", - "898c5dee9dfe4d6f91c45d78d1db175d", - "048694acceb74a18951fa708f2f028e6", - "8c1b753382db42e2a866bdfbb01afe4e", - "197aa3fa80654c3ca522eb90ca415d1c", - "652677eb9a624670b52b9c0fca2446dc", - "10c78319b1ce4f0384730deddb3ac7d1", - "ad8a48fd64304636aea82679f319cdad", - "c315820ce783429ea0c477a3eb090424", - "c534c9a03c0a4862b30e85bfd7b0d5e6", - "da576a9bef6b44179eb48896038628c8", - "77c910538b534c829969b5daaa01d568", - "e419843474154e8db8e30c488438a80a", - "3b8ca764c5ec4271a03559b5cedf7fb5", - "2f05720b4750496081b707a411c4c2c8", - "0cea8e336ffd4abb9b6d5691e0c135fc", - "aa389d3b7d874722b696798ec9bc332b", - "3c9c1a2d3b824702be2ec3ddfacdd9ca", - "fa3427a8d2cd4aedb906022770be35d5" - ] - }, - "id": "5a10cc4b-07ec-4ebd-ac1d-7c601023594f", - "outputId": "cab4e4cb-8252-4200-cc52-afc89359c3bf" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "config.json: 0%| | 0.00/1.99k [00:00" - ] - }, - "metadata": {}, - "execution_count": 29 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Vjl4j4RJPmPR" - }, - "source": [ - "### Apply Low-rank adapters (LoRA) to the model\n", - "\n", - "Here comes the magic with `peft`! Let's load a `PeftModel` and specify that we are going to use low-rank adapters (LoRA) using `get_peft_model` utility function from `peft`." - ], - "id": "Vjl4j4RJPmPR" - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "DQtpDPRHPyOL", - "outputId": "0cdfaa10-4f66-486b-f23b-6813b223035a" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "trainable params: 15,728,640 || all params: 1,559,033,600 || trainable%: 1.0089\n" - ] - } - ], - "source": [ - "from peft import LoraConfig, PeftModel, LoraModel, LoraConfig, get_peft_model\n", - "\n", - "config = LoraConfig(r=32, lora_alpha=64, target_modules=[\"q_proj\", \"v_proj\"], lora_dropout=0.05, bias=\"none\")\n", - "\n", - "model = get_peft_model(model, config)\n", - "model.print_trainable_parameters()" - ], - "id": "DQtpDPRHPyOL" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3906d436" - }, - "source": [ - "We are ONLY using **1%** of the total trainable parameters, thereby performing **Parameter-Efficient Fine-Tuning**" - ], - "id": "3906d436" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "2178dea4-80ca-47b6-b6ea-ba1915c90c06" - }, - "source": [ - "### Define the Training Configuration" - ], - "id": "2178dea4-80ca-47b6-b6ea-ba1915c90c06" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "c21af1e9-0188-4134-ac82-defc7bdcc436" - }, - "source": [ - "In the final step, we define all the parameters related to training. For more detail on the training arguments, refer to the Seq2SeqTrainingArguments [docs](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.Seq2SeqTrainingArguments)." - ], - "id": "c21af1e9-0188-4134-ac82-defc7bdcc436" - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "id": "0ae3e9af-97b7-4aa0-ae85-20b23b5bcb3a", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "a21be6fb-7f3f-4e5f-c360-9e1ce88b30b3" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1493: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n", - " warnings.warn(\n" - ] - } - ], - "source": [ - "from transformers import Seq2SeqTrainingArguments\n", - "\n", - "training_args = Seq2SeqTrainingArguments(\n", - " output_dir=\"temp\", # change to a repo name of your choice\n", - " per_device_train_batch_size=8,\n", - " gradient_accumulation_steps=1, # increase by 2x for every 2x decrease in batch size\n", - " learning_rate=1e-3,\n", - " warmup_steps=50,\n", - " num_train_epochs=2,\n", - " evaluation_strategy=\"epoch\",\n", - " fp16=True,\n", - " per_device_eval_batch_size=8,\n", - " generation_max_length=128,\n", - " logging_steps=25,\n", - " remove_unused_columns=False, # required as the PeftModel forward doesn't have the signature of the wrapped model's forward\n", - " label_names=[\"labels\"], # same reason as above\n", - ")" - ], - "id": "0ae3e9af-97b7-4aa0-ae85-20b23b5bcb3a" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "b3a944d8-3112-4552-82a0-be25988b3857" - }, - "source": [ - "Fine-tuning a model with PEFT comes with a few caveats.\n", - "\n", - "1. We need to explicitly set `remove_unused_columns=False` and `label_names=[\"labels\"]` as the PeftModel's forward doesn't inherit the signature of the base model's forward.\n", - "\n", - "2. Since INT8 training requires autocasting, we cannot use the native `predict_with_generate` call in Trainer as it doesn't automatically cast.\n", - "\n", - "3. Similarly, since we cannot autocast, we cannot pass the `compute_metrics` to `Seq2SeqTrainer` so we'll comment it out whilst instantiating the Trainer." - ], - "id": "b3a944d8-3112-4552-82a0-be25988b3857" - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "id": "d546d7fe-0543-479a-b708-2ebabec19493" - }, - "outputs": [], - "source": [ - "from transformers import Seq2SeqTrainer, TrainerCallback, TrainingArguments, TrainerState, TrainerControl\n", - "from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR\n", - "\n", - "# This callback helps to save only the adapter weights and remove the base model weights.\n", - "class SavePeftModelCallback(TrainerCallback):\n", - " def on_save(\n", - " self,\n", - " args: TrainingArguments,\n", - " state: TrainerState,\n", - " control: TrainerControl,\n", - " **kwargs,\n", - " ):\n", - " checkpoint_folder = os.path.join(args.output_dir, f\"{PREFIX_CHECKPOINT_DIR}-{state.global_step}\")\n", - "\n", - " peft_model_path = os.path.join(checkpoint_folder, \"adapter_model\")\n", - " kwargs[\"model\"].save_pretrained(peft_model_path)\n", - "\n", - " pytorch_model_path = os.path.join(checkpoint_folder, \"pytorch_model.bin\")\n", - " if os.path.exists(pytorch_model_path):\n", - " os.remove(pytorch_model_path)\n", - " return control\n", - "\n", - "\n", - "trainer = Seq2SeqTrainer(\n", - " args=training_args,\n", - " model=model,\n", - " train_dataset=common_voice[\"train\"],\n", - " eval_dataset=common_voice[\"test\"],\n", - " data_collator=data_collator,\n", - " # compute_metrics=compute_metrics,\n", - " tokenizer=processor.feature_extractor,\n", - " callbacks=[SavePeftModelCallback],\n", - ")\n", - "model.config.use_cache = False # silence the warnings. Please re-enable for inference!" - ], - "id": "d546d7fe-0543-479a-b708-2ebabec19493" - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 303 - }, - "id": "ee8b7b8e-1c9a-4d77-9137-1778a629e6de", - "outputId": "0b67d521-bce9-46d4-c8dd-e4fdb85b8119" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n", - " warnings.warn(\n", - "/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:316: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n", - " warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
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" - ] - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:316: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n", - " warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "TrainOutput(global_step=500, training_loss=0.2784323353767395, metrics={'train_runtime': 7399.1704, 'train_samples_per_second': 0.541, 'train_steps_per_second': 0.068, 'total_flos': 8.5832810496e+18, 'train_loss': 0.2784323353767395, 'epoch': 2.0})" - ] - }, - "metadata": {}, - "execution_count": 33 - } - ], - "source": [ - "trainer.train()" - ], - "id": "ee8b7b8e-1c9a-4d77-9137-1778a629e6de" - }, - { - "cell_type": "code", - "source": [ - "peft_model_id = \"kawther1/whisperlargeev2\"\n", - "model.push_to_hub(peft_model_id)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 85, - "referenced_widgets": [ - "a87effb0d99d48279ba8ea3dd3b0dbf9", - "2aaac7e5e00942fa97998e0a837aca1a", - "dc5cd737ab0946d98c7a2f90809e6f6d", - "f1e60435a08f4b27b186bc69b01d615e", - "1ead702dd45a471e9b10db4888e1e547", - "d9d43996ab1a4c68965286933cec88af", - "110ced8d91634d9cbfa9719c476f9eb9", - "90108def19bc43d09d459b82da5137e9", - "bd910f94d83d46a98e4504298644bdaf", - "2b20855170a24e519f29b30d9c017edc", - "204ecf3fcfbd419fabec5af1339ea033" - ] - }, - "id": "lf7oCLJAK0kv", - "outputId": "afa215b6-9ea3-4eab-f2d7-709d7c69fe33" - }, - "id": "lf7oCLJAK0kv", - "execution_count": 36, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "adapter_model.safetensors: 0%| | 0.00/63.0M [00:00
Copy a token from your Hugging Face\ntokens page and paste it below.
Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file.

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