peft_bnb_whisper_large_v2_training.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Timothy102/whisper_lora/blob/main/peft_bnb_whisper_large_v2_training.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5cefac89"
      },
      "source": [
        "# Finetuning Whisper-large-V2 on Colab using PEFT-Lora + BNB INT8 training"
      ],
      "id": "5cefac89"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "090fa3ed"
      },
      "source": [
        "In this Colab, we present a step-by-step guide on how to fine-tune Whisper for any multilingual ASR dataset using Hugging Face 🤗 Transformers and 🤗 PEFT. Using 🤗 PEFT and `bitsandbytes`, you can train the `whisper-large-v2` seamlessly on a colab with T4 GPU (16 GB VRAM). In this notebook, with most parts from [fine_tune_whisper.ipynb](https://colab.research.google.com/github/sanchit-gandhi/notebooks/blob/main/fine_tune_whisper.ipynb#scrollTo=BRdrdFIeU78w) is adapted to train using PEFT LoRA+BNB INT8.\n",
        "\n",
        "For more details on model, datasets and metrics, refer blog [Fine-Tune Whisper For Multilingual ASR with 🤗 Transformers](https://huggingface.co/blog/fine-tune-whisper)\n",
        "\n"
      ],
      "id": "090fa3ed"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "625e47a0"
      },
      "source": [
        "## Inital Setup"
      ],
      "id": "625e47a0"
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eJrPyQM5Xhv5",
        "outputId": "9b9c93cc-43e2-4a13-dc4b-cc7484e01376"
      },
      "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",
            "Get:2 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64  InRelease [1,581 B]\n",
            "Get:3 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64  Packages [673 kB]\n",
            "Hit:4 http://archive.ubuntu.com/ubuntu jammy InRelease\n",
            "Get:5 http://security.ubuntu.com/ubuntu jammy-security InRelease [110 kB]\n",
            "Get:6 http://archive.ubuntu.com/ubuntu jammy-updates InRelease [119 kB]\n",
            "Hit:7 https://ppa.launchpadcontent.net/c2d4u.team/c2d4u4.0+/ubuntu jammy InRelease\n",
            "Hit:8 https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu jammy InRelease\n",
            "Hit:9 https://ppa.launchpadcontent.net/graphics-drivers/ppa/ubuntu jammy InRelease\n",
            "Ign:10 https://ppa.launchpadcontent.net/jonathonf/ffmpeg-4/ubuntu jammy InRelease\n",
            "Hit:11 https://ppa.launchpadcontent.net/ubuntugis/ppa/ubuntu jammy InRelease\n",
            "Err:12 https://ppa.launchpadcontent.net/jonathonf/ffmpeg-4/ubuntu jammy Release\n",
            "  404  Not Found [IP: 185.125.190.80 443]\n",
            "Get:13 http://security.ubuntu.com/ubuntu jammy-security/restricted amd64 Packages [1,751 kB]\n",
            "Get:14 http://archive.ubuntu.com/ubuntu jammy-backports InRelease [109 kB]\n",
            "Get:15 http://archive.ubuntu.com/ubuntu jammy-updates/restricted amd64 Packages [1,810 kB]\n",
            "Get:16 http://security.ubuntu.com/ubuntu jammy-security/main amd64 Packages [1,440 kB]\n",
            "Get:17 http://security.ubuntu.com/ubuntu jammy-security/universe amd64 Packages [1,065 kB]\n",
            "Get:18 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 Packages [1,335 kB]\n",
            "Get:19 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 Packages [1,728 kB]\n",
            "Get:20 http://archive.ubuntu.com/ubuntu jammy-backports/universe amd64 Packages [28.1 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 http://security.ubuntu.com/ubuntu jammy-security InRelease\n",
            "Hit:4 http://archive.ubuntu.com/ubuntu jammy InRelease\n",
            "Hit:5 https://ppa.launchpadcontent.net/c2d4u.team/c2d4u4.0+/ubuntu jammy InRelease\n",
            "Hit:6 http://archive.ubuntu.com/ubuntu jammy-updates InRelease\n",
            "Hit:7 https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu jammy InRelease\n",
            "Hit:8 http://archive.ubuntu.com/ubuntu jammy-backports InRelease\n",
            "Hit:9 https://ppa.launchpadcontent.net/graphics-drivers/ppa/ubuntu jammy InRelease\n",
            "Ign:10 https://ppa.launchpadcontent.net/jonathonf/ffmpeg-4/ubuntu jammy InRelease\n",
            "Hit:11 https://ppa.launchpadcontent.net/ubuntugis/ppa/ubuntu jammy InRelease\n",
            "Err:12 https://ppa.launchpadcontent.net/jonathonf/ffmpeg-4/ubuntu jammy Release\n",
            "  404  Not Found [IP: 185.125.190.80 443]\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 35 not upgraded.\n"
          ]
        }
      ],
      "source": [
        "!add-apt-repository -y ppa:jonathonf/ffmpeg-4\n",
        "!apt update\n",
        "!apt install -y ffmpeg"
      ],
      "id": "eJrPyQM5Xhv5"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r_Ivl7qlX0dz",
        "outputId": "b4da8a71-d373-4115-a182-1b424eb2f17e"
      },
      "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",
            "ibis-framework 7.1.0 requires pyarrow<15,>=2, but you have pyarrow 15.0.0 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-i_7xo_pp\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/huggingface/transformers /tmp/pip-req-build-i_7xo_pp\n",
            "  Resolved https://github.com/huggingface/transformers to commit de11e654c962d5b23eb53a4387cd637b01987491\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==4.38.0.dev0) (3.13.1)\n",
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            "Building wheels for collected packages: transformers\n",
            "  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for transformers: filename=transformers-4.38.0.dev0-py3-none-any.whl size=8458687 sha256=49e1b20754341ab98b6d77b5491e66d575a534f17ffc8dc1b509e060fae4d0d8\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-lkjitie0/wheels/c0/14/d6/6c9a5582d2ac191ec0a483be151a4495fe1eb2a6706ca49f1b\n",
            "Successfully built transformers\n",
            "Installing collected packages: transformers\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.35.2\n",
            "    Uninstalling transformers-4.35.2:\n",
            "      Successfully uninstalled transformers-4.35.2\n",
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            "  Downloading websockets-11.0.3-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (129 kB)\n",
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            "Collecting colorama<0.5.0,>=0.4.3 (from typer[all]<1.0,>=0.9->gradio)\n",
            "  Downloading colorama-0.4.6-py2.py3-none-any.whl (25 kB)\n",
            "Collecting shellingham<2.0.0,>=1.3.0 (from typer[all]<1.0,>=0.9->gradio)\n",
            "  Downloading shellingham-1.5.4-py2.py3-none-any.whl (9.8 kB)\n",
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            "Collecting h11>=0.8 (from uvicorn>=0.14.0->gradio)\n",
            "  Downloading h11-0.14.0-py3-none-any.whl (58 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m10.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting starlette<0.37.0,>=0.36.3 (from fastapi->gradio)\n",
            "  Downloading starlette-0.36.3-py3-none-any.whl (71 kB)\n",
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            "Building wheels for collected packages: ffmpy\n",
            "  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ffmpy: filename=ffmpy-0.3.1-py3-none-any.whl size=5579 sha256=0054c12a44a569510268c97443eb24707854cfe1be169d6299d638af3d6b9503\n",
            "  Stored in directory: /root/.cache/pip/wheels/01/a6/d1/1c0828c304a4283b2c1639a09ad86f83d7c487ef34c6b4a1bf\n",
            "Successfully built ffmpy\n",
            "Installing collected packages: pydub, ffmpy, websockets, tomlkit, shellingham, semantic-version, ruff, python-multipart, orjson, h11, colorama, aiofiles, uvicorn, starlette, httpcore, httpx, fastapi, gradio-client, gradio\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",
            "lida 0.0.10 requires kaleido, which is not installed.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed aiofiles-23.2.1 colorama-0.4.6 fastapi-0.109.2 ffmpy-0.3.1 gradio-4.17.0 gradio-client-0.9.0 h11-0.14.0 httpcore-1.0.2 httpx-0.26.0 orjson-3.9.13 pydub-0.25.1 python-multipart-0.0.7 ruff-0.2.1 semantic-version-2.10.0 shellingham-1.5.4 starlette-0.36.3 tomlkit-0.12.0 uvicorn-0.27.0.post1 websockets-11.0.3\n",
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            "\u001b[?25h  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",
            "  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"
          ]
        }
      ],
      "source": [
        "!pip install datasets>=2.6.1\n",
        "!pip install git+https://github.com/huggingface/transformers\n",
        "!pip install librosa\n",
        "!pip install evaluate>=0.30\n",
        "!pip install jiwer\n",
        "!pip install gradio\n",
        "!pip install -q bitsandbytes datasets accelerate loralib\n",
        "!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git@main"
      ],
      "id": "r_Ivl7qlX0dz"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8a528c1a"
      },
      "source": [
        "Linking the notebook to the Hub is straightforward - it simply requires entering your Hub authentication token when prompted. Find your Hub authentication token [here](https://huggingface.co/settings/tokens):"
      ],
      "id": "8a528c1a"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "17f31bd5ae404491ad9fc2f49a8406ec",
            "813efe71f3484ce9a5dd32c23f3058d4",
            "ce4b41892f934eb28a01649e8d092f82",
            "bfff6897753446549d8889aec1812901",
            "3cb1c91dcd1f4f87b65734a003e38637",
            "91d6fd32564b42bf86d752816a0ca9ce",
            "26ab7471ff9543a18fbe1745d00e751f",
            "d5a418aff6074820a724d3cf305fca00",
            "cf422d43fea0439caed311a0827a640c",
            "1b9945c2cd1f4a719fa23dc871cf0a87",
            "6b7f702d9bee4cc59b202ec55eb2ef48",
            "282eb983dfe04c5db7edec5fe4881e45",
            "5b77cee653fd473d80cb54278144758b",
            "1cd9112bd7244923a4904e446f5dfb49",
            "5726241d191140c8a893e5d7046f79b5",
            "a60a34b69024418cba4b392fb285ef88",
            "127bcb26e0994a1da282ecd3440ed4d5",
            "a05258050f1e42809f2876964de5ac08",
            "ce95d2713f01489696754d488fc935cb",
            "dbaeb5ced694402fa86e65af529a3ec3",
            "e6d64609bbd8441b9b862a7e07f2617c",
            "b53a8b6a90b94909bad2623c2fdb3620",
            "f6c8b15a131349c7ae2ad3555d92b1b5",
            "eb681c15399441bcb91783ab0f86d819",
            "8bd2348959d14d23ba23da411d6758d5",
            "c16f4928c47d44dea1e72a0433d74575",
            "3bdc310d15074797be08641561224041",
            "3e6e17912c4246b68069e71c13f22371",
            "5f36c4984a6e499b8ccfdd9c6344e230",
            "7aaf73bcc79a4285834287507330c8fd",
            "ca5ac40905a14a809e331969f57594a0",
            "d4d0d48fe76941a3b030bdaefea376fe"
          ]
        },
        "id": "ed0OpduhX2JF",
        "outputId": "e700fed7-b71f-4eaa-ef14-e25324a38914"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "17f31bd5ae404491ad9fc2f49a8406ec"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ],
      "id": "ed0OpduhX2JF"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "e1da5fff"
      },
      "outputs": [],
      "source": [
        "# Select CUDA device index\n",
        "import os\n",
        "\n",
        "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
        "model_name_or_path = \"openai/whisper-large-v2\"\n",
        "language = \"Marathi\"\n",
        "language_abbr = \"mr\"\n",
        "task = \"transcribe\"\n",
        "dataset_name = \"mozilla-foundation/common_voice_11_0\""
      ],
      "id": "e1da5fff"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "805b1c56"
      },
      "source": [
        "## Load Dataset"
      ],
      "id": "805b1c56"
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/datasets/load.py:2508: FutureWarning: 'use_auth_token' was deprecated in favor of 'token' in version 2.14.0 and will be removed in 3.0.0.\n",
            "You can remove this warning by passing 'token=<use_auth_token>' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: 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:1454: FutureWarning: The repository for mozilla-foundation/common_voice_11_0 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_11_0\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"
          ]
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "9c8d216cfaa049d88622033eecef651f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "Reading metadata...: 2237it [00:00, 68337.91it/s]\n",
            "/usr/local/lib/python3.10/dist-packages/datasets/load.py:2508: FutureWarning: 'use_auth_token' was deprecated in favor of 'token' in version 2.14.0 and will be removed in 3.0.0.\n",
            "You can remove this warning by passing 'token=<use_auth_token>' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/datasets/load.py:1454: FutureWarning: The repository for mozilla-foundation/common_voice_11_0 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_11_0\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'],\n",
            "        num_rows: 3927\n",
            "    })\n",
            "    test: Dataset({\n",
            "        features: ['client_id', 'path', 'audio', 'sentence', 'up_votes', 'down_votes', 'age', 'gender', 'accent', 'locale', 'segment'],\n",
            "        num_rows: 1816\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+validation\", use_auth_token=True)\n",
        "common_voice[\"test\"] = load_dataset(dataset_name, language_abbr, split=\"test\", use_auth_token=True)\n",
        "\n",
        "print(common_voice)"
      ],
      "id": "a2787582-554f-44ce-9f38-4180a5ed6b44"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "20ba635d-518c-47ac-97ee-3cad25f1e0ce",
        "outputId": "8c39a269-affe-43f7-8ce9-adfbcf739eda"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DatasetDict({\n",
            "    train: Dataset({\n",
            "        features: ['audio', 'sentence'],\n",
            "        num_rows: 3927\n",
            "    })\n",
            "    test: Dataset({\n",
            "        features: ['audio', 'sentence'],\n",
            "        num_rows: 1816\n",
            "    })\n",
            "})\n"
          ]
        }
      ],
      "source": [
        "common_voice = common_voice.remove_columns(\n",
        "    [\"accent\", \"age\", \"client_id\", \"down_votes\", \"gender\", \"locale\", \"path\", \"segment\", \"up_votes\"]\n",
        ")\n",
        "\n",
        "print(common_voice)"
      ],
      "id": "20ba635d-518c-47ac-97ee-3cad25f1e0ce"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2d63b2d2-f68a-4d74-b7f1-5127f6d16605"
      },
      "source": [
        "## Prepare Feature Extractor, Tokenizer and Data"
      ],
      "id": "2d63b2d2-f68a-4d74-b7f1-5127f6d16605"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "d81d0112acce4b7f9ef5204496273ddc",
            "875d9c368afa49fea085b30bd290d322",
            "ddcdcf54314842aeafc6a47019f7e8f0",
            "73775a708adf45e6afaea875ece58181",
            "d49dfd923b2e4b1e8faee923ef6ca078",
            "00afec802d1747b6b2352f7bdd770cce",
            "9ec0c5ec79ad426e88856f81aabd734c",
            "57971085cfc4402495a6fe697f1cc8d4",
            "1226c1048d7b4cefbb317f37fdbc9322",
            "860ec00cef8d45a19548b00abaa5a0c7",
            "0ffe149336a84b96989d658001bcefe3"
          ]
        },
        "id": "bc77d7bb-f9e2-47f5-b663-30f7a4321ce5",
        "outputId": "8a18fbef-418c-4404-840a-78198af42d51"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "preprocessor_config.json:   0%|          | 0.00/185k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d81d0112acce4b7f9ef5204496273ddc"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import WhisperFeatureExtractor\n",
        "\n",
        "feature_extractor = WhisperFeatureExtractor.from_pretrained(model_name_or_path)"
      ],
      "id": "bc77d7bb-f9e2-47f5-b663-30f7a4321ce5"
    },
    {
      "cell_type": "code",
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            "3131a1125eb74666887beb73381cd428",
            "527d3a3fb7284f8b836dffddb0b2c12b",
            "eac8cc61aaf84119b4f51019d8f673e0",
            "d0bb74fda310424eafec931e605f69f5",
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            "2a08777b03bc4e05aa42baa8b46f216f",
            "383b2ce19a4e46fab9e3c1343c70fd1d",
            "24f364ba73934eed9137c81d39b8436c",
            "e620e91f393244ddb32a32d059b94047"
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        },
        "id": "c7b07f9b-ae0e-4f89-98f0-0c50d432eab6",
        "outputId": "225c4753-07dd-4fbf-e763-2c4dc95882ae"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/805 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b97d35a2799641a1986ee7f6cd62893e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e03e89f6e60f474cb2b8b98759caad50"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/2.48M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e112c1bb5b33426abea46e257e1277ba"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "merges.txt:   0%|          | 0.00/494k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "38c8a7d3533f47e3bb3b8657e3e0be4a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "normalizer.json:   0%|          | 0.00/52.7k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "988ec4d47b804e5a857b757e7a9d0d14"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "added_tokens.json:   0%|          | 0.00/34.6k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "067c846478da4550b0580c2ddb055f2c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/2.08k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "eea95891ca4e4b7d9c9dca70a67cf95b"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import WhisperTokenizer\n",
        "\n",
        "tokenizer = WhisperTokenizer.from_pretrained(model_name_or_path, language=language, task=task)"
      ],
      "id": "c7b07f9b-ae0e-4f89-98f0-0c50d432eab6"
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "77d9f0c5-8607-4642-a8ac-c3ab2e223ea6"
      },
      "outputs": [],
      "source": [
        "from transformers import WhisperProcessor\n",
        "\n",
        "processor = WhisperProcessor.from_pretrained(model_name_or_path, language=language, task=task)"
      ],
      "id": "77d9f0c5-8607-4642-a8ac-c3ab2e223ea6"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "381acd09-0b0f-4d04-9eb3-f028ac0e5f2c"
      },
      "source": [
        "### Prepare Data"
      ],
      "id": "381acd09-0b0f-4d04-9eb3-f028ac0e5f2c"
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6e6b0ec5-0c94-4e2c-ae24-c791be1b2255",
        "outputId": "d2eaefe1-a391-4d56-beb4-5df9c9cb4969"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'audio': {'path': '/root/.cache/huggingface/datasets/downloads/extracted/f7e1ef6a2d14f20194999aad5040c5d4bb3ead1377de3e1bbc6e9dba34d18a8a/mr_train_0/common_voice_mr_30585613.mp3', 'array': array([ 1.13686838e-13, -1.42108547e-13, -1.98951966e-13, ...,\n",
            "        4.83472422e-06,  3.54798703e-06,  1.63231743e-06]), 'sampling_rate': 48000}, 'sentence': 'आईचे आजारपण वाढत चालले, तसतशी मथीही नीट खातपीतनाशी झाली.'}\n"
          ]
        }
      ],
      "source": [
        "print(common_voice[\"train\"][0])"
      ],
      "id": "6e6b0ec5-0c94-4e2c-ae24-c791be1b2255"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5a679f05-063d-41b3-9b58-4fc9c6ccf4fd"
      },
      "source": [
        "Since\n",
        "our input audio is sampled at 48kHz, we need to _downsample_ it to\n",
        "16kHz prior to passing it to the Whisper feature extractor, 16kHz being the sampling rate expected by the Whisper model.\n",
        "\n",
        "We'll set the audio inputs to the correct sampling rate using dataset's\n",
        "[`cast_column`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=cast_column#datasets.DatasetDict.cast_column)\n",
        "method. This operation does not change the audio in-place,\n",
        "but rather signals to `datasets` to resample audio samples _on the fly_ the\n",
        "first time that they are loaded:"
      ],
      "id": "5a679f05-063d-41b3-9b58-4fc9c6ccf4fd"
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "f12e2e57-156f-417b-8cfb-69221cc198e8"
      },
      "outputs": [],
      "source": [
        "from datasets import Audio\n",
        "\n",
        "common_voice = common_voice.cast_column(\"audio\", Audio(sampling_rate=16000))"
      ],
      "id": "f12e2e57-156f-417b-8cfb-69221cc198e8"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "00382a3e-abec-4cdd-a54c-d1aaa3ea4707"
      },
      "source": [
        "Re-loading the first audio sample in the Common Voice dataset will resample\n",
        "it to the desired sampling rate:"
      ],
      "id": "00382a3e-abec-4cdd-a54c-d1aaa3ea4707"
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "87122d71-289a-466a-afcf-fa354b18946b",
        "outputId": "a6a1486f-d16c-44b0-b5f5-3ca3a2365655"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'audio': {'path': '/root/.cache/huggingface/datasets/downloads/extracted/f7e1ef6a2d14f20194999aad5040c5d4bb3ead1377de3e1bbc6e9dba34d18a8a/mr_train_0/common_voice_mr_30585613.mp3', 'array': array([-3.06954462e-12, -3.63797881e-12, -4.54747351e-12, ...,\n",
            "       -7.74800901e-06, -1.74738125e-06,  4.36312439e-06]), 'sampling_rate': 16000}, 'sentence': 'आईचे आजारपण वाढत चालले, तसतशी मथीही नीट खातपीतनाशी झाली.'}\n"
          ]
        }
      ],
      "source": [
        "print(common_voice[\"train\"][0])"
      ],
      "id": "87122d71-289a-466a-afcf-fa354b18946b"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "91edc72d-08f8-4f01-899d-74e65ce441fc"
      },
      "source": [
        "Now we can write a function to prepare our data ready for the model:\n",
        "1. We load and resample the audio data by calling `batch[\"audio\"]`. As explained above, 🤗 Datasets performs any necessary resampling operations on the fly.\n",
        "2. We use the feature extractor to compute the log-Mel spectrogram input features from our 1-dimensional audio array.\n",
        "3. We encode the transcriptions to label ids through the use of the tokenizer."
      ],
      "id": "91edc72d-08f8-4f01-899d-74e65ce441fc"
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "6525c478-8962-4394-a1c4-103c54cce170"
      },
      "outputs": [],
      "source": [
        "def prepare_dataset(batch):\n",
        "    # load and resample audio data from 48 to 16kHz\n",
        "    audio = batch[\"audio\"]\n",
        "\n",
        "    # compute log-Mel input features from input audio array\n",
        "    batch[\"input_features\"] = feature_extractor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_features[0]\n",
        "\n",
        "    # encode target text to label ids\n",
        "    batch[\"labels\"] = tokenizer(batch[\"sentence\"]).input_ids\n",
        "    return batch"
      ],
      "id": "6525c478-8962-4394-a1c4-103c54cce170"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "70b319fb-2439-4ef6-a70d-a47bf41c4a13"
      },
      "source": [
        "We can apply the data preparation function to all of our training examples using dataset's `.map` method. The argument `num_proc` specifies how many CPU cores to use. Setting `num_proc` > 1 will enable multiprocessing. If the `.map` method hangs with multiprocessing, set `num_proc=1` and process the dataset sequentially."
      ],
      "id": "70b319fb-2439-4ef6-a70d-a47bf41c4a13"
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "100a9127e2804247a9b2f7ac0059bb1e",
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            "60b6fd34b63f4b0a9239081965b07ecd",
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            "11b281000e144ab4a72591af6b3aa452",
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            "7990e137d0c9439ba13b382edb425926",
            "b222d45428f446df971958072feab579",
            "60396924eb684800af440d5c3b940cb0",
            "7a6120b0afae49d9a6dc2b3555045797",
            "de4a0dd6d1f5491faf370e9b0a205ebb",
            "30e981df00df4d65a63c11d23e52ab0c",
            "b41862f37e8541048dfea9a984868675",
            "b747400b0c634644b62f25222934ffe2",
            "6c039cf147c8481f85d65a442add874c",
            "f2c013170b0b45968127005b9ca7a146",
            "ed1cd6c0f6d4405894e0ec68bf5d2bb8",
            "e8137ca394424d3facbadd75ecb0af93"
          ]
        },
        "id": "7b73ab39-ffaf-4b9e-86e5-782963c6134b",
        "outputId": "551851c7-04e3-4f78-bd9d-d8b94b09bc8d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/3927 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "100a9127e2804247a9b2f7ac0059bb1e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1816 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b222d45428f446df971958072feab579"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice.column_names[\"train\"], num_proc=1)"
      ],
      "id": "7b73ab39-ffaf-4b9e-86e5-782963c6134b"
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c4be572c",
        "outputId": "86137fc7-1728-4831-f9af-29897b4673e2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['input_features', 'labels'],\n",
              "    num_rows: 3927\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "common_voice[\"train\"]"
      ],
      "id": "c4be572c"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "263a5a58-0239-4a25-b0df-c625fc9c5810"
      },
      "source": [
        "## Training and Evaluation"
      ],
      "id": "263a5a58-0239-4a25-b0df-c625fc9c5810"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8d230e6d-624c-400a-bbf5-fa660881df25"
      },
      "source": [
        "### Define a Data Collator"
      ],
      "id": "8d230e6d-624c-400a-bbf5-fa660881df25"
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "8326221e-ec13-4731-bb4e-51e5fc1486c5"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "\n",
        "from dataclasses import dataclass\n",
        "from typing import Any, Dict, List, Union\n",
        "\n",
        "\n",
        "@dataclass\n",
        "class DataCollatorSpeechSeq2SeqWithPadding:\n",
        "    processor: Any\n",
        "\n",
        "    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:\n",
        "        # split inputs and labels since they have to be of different lengths and need different padding methods\n",
        "        # first treat the audio inputs by simply returning torch tensors\n",
        "        input_features = [{\"input_features\": feature[\"input_features\"]} for feature in features]\n",
        "        batch = self.processor.feature_extractor.pad(input_features, return_tensors=\"pt\")\n",
        "\n",
        "        # get the tokenized label sequences\n",
        "        label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n",
        "        # pad the labels to max length\n",
        "        labels_batch = self.processor.tokenizer.pad(label_features, return_tensors=\"pt\")\n",
        "\n",
        "        # replace padding with -100 to ignore loss correctly\n",
        "        labels = labels_batch[\"input_ids\"].masked_fill(labels_batch.attention_mask.ne(1), -100)\n",
        "\n",
        "        # if bos token is appended in previous tokenization step,\n",
        "        # cut bos token here as it's append later anyways\n",
        "        if (labels[:, 0] == self.processor.tokenizer.bos_token_id).all().cpu().item():\n",
        "            labels = labels[:, 1:]\n",
        "\n",
        "        batch[\"labels\"] = labels\n",
        "\n",
        "        return batch"
      ],
      "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": 17,
      "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": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "9f08cfeeb5f44234884001535a891333",
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            "95e4ca4987e240e79c11876399828bee",
            "a4132e9c4c974997a1f525675d06e42c",
            "0a81507c39ab4dd4867e7a08cf78dfd0",
            "9a6922d5f42a4c8ca0aa199ead29336b"
          ]
        },
        "id": "b22b4011-f31f-4b57-b684-c52332f92890",
        "outputId": "c4a158d1-2d2c-4185-a12a-075f3a9af5be"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/4.49k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9f08cfeeb5f44234884001535a891333"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "import evaluate\n",
        "\n",
        "metric = evaluate.load(\"wer\")"
      ],
      "id": "b22b4011-f31f-4b57-b684-c52332f92890"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4f32cab6-31f0-4cb9-af4c-40ba0f5fc508"
      },
      "source": [
        "We then simply have to define a function that takes our model\n",
        "predictions and returns the WER metric. This function, called\n",
        "`compute_metrics`, first replaces `-100` with the `pad_token_id`\n",
        "in the `label_ids` (undoing the step we applied in the\n",
        "data collator to ignore padded tokens correctly in the loss).\n",
        "It then decodes the predicted and label ids to strings. Finally,\n",
        "it computes the WER between the predictions and reference labels:"
      ],
      "id": "4f32cab6-31f0-4cb9-af4c-40ba0f5fc508"
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "23959a70-22d0-4ffe-9fa1-72b61e75bb52"
      },
      "outputs": [],
      "source": [
        "def compute_metrics(pred):\n",
        "    pred_ids = pred.predictions\n",
        "    label_ids = pred.label_ids\n",
        "\n",
        "    # replace -100 with the pad_token_id\n",
        "    label_ids[label_ids == -100] = tokenizer.pad_token_id\n",
        "\n",
        "    # we do not want to group tokens when computing the metrics\n",
        "    pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)\n",
        "    label_str = tokenizer.batch_decode(label_ids, skip_special_tokens=True)\n",
        "\n",
        "    wer = 100 * metric.compute(predictions=pred_str, references=label_str)\n",
        "\n",
        "    return {\"wer\": wer}"
      ],
      "id": "23959a70-22d0-4ffe-9fa1-72b61e75bb52"
    },
    {
      "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 `small` checkpoint. Again, this\n",
        "is trivial through use of 🤗 Transformers!"
      ],
      "id": "437a97fa-4864-476b-8abc-f28b8166cfa5"
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 150,
          "referenced_widgets": [
            "7d50602ac9f84cf1bebb96ae216ab570",
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            "fa90399801d546bc83e1577bbe590516",
            "71887c94639243eb9d0692a91a57c213",
            "ede7ff1a03d841d6ba38c54eca49fdfe",
            "95a5e6941d6e402fb5e33e92c08d7194",
            "470e8f7ec10e401397de5ac0d89120b2",
            "eda83c29b9404a348d641f14319b7526",
            "9d77f051816545d4bcd161cde9f20693",
            "b36b1b0601854ff2aed1bc59239b6631",
            "223be46ba764430e9c028460e9777f3b",
            "f6cf0577ee894f41a65e1fff44517b99",
            "ed36c50e53694b98a4209ed3fcad8b3c",
            "a4f6f67136e745c1a5bfcb9d96c85cb7",
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            "37263bb862cb45dda07f158a333c8eee",
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            "16bc605701bb4fe39529c25aa5ca66b0",
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            "05cb0b5822654ddbaa2c0c414ac0020f",
            "17f78f9c7e484557ad5e011b3c635e97"
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        },
        "id": "5a10cc4b-07ec-4ebd-ac1d-7c601023594f",
        "outputId": "66296aee-0d85-4572-d67c-73f21dcd059f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.99k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7d50602ac9f84cf1bebb96ae216ab570"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The `load_in_4bit` and `load_in_8bit` arguments are deprecated and will be removed in the future versions. Please, pass a `BitsAndBytesConfig` object in `quantization_config` argument instead.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/6.17G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "223be46ba764430e9c028460e9777f3b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/4.29k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cf77e51d2c314ee98077352d64f48848"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import WhisperForConditionalGeneration\n",
        "\n",
        "model = WhisperForConditionalGeneration.from_pretrained(model_name_or_path, load_in_8bit=True, device_map=\"auto\")\n",
        "\n",
        "# model.hf_device_map - this should be {\" \": 0}"
      ],
      "id": "5a10cc4b-07ec-4ebd-ac1d-7c601023594f"
    },
    {
      "cell_type": "markdown",
      "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)):"
      ],
      "id": "a15ead5f-2277-4a39-937b-585c2497b2df"
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "62038ba3-88ed-4fce-84db-338f50dcd04f"
      },
      "outputs": [],
      "source": [
        "model.config.forced_decoder_ids = None\n",
        "model.config.suppress_tokens = []"
      ],
      "id": "62038ba3-88ed-4fce-84db-338f50dcd04f"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bR-_yaEOPsfQ"
      },
      "source": [
        "### Post-processing on the model\n",
        "\n",
        "Finally, we need to apply some post-processing on the 8-bit model to enable training, let's freeze all our layers, and cast the layer-norm in `float32` for stability. We also cast the output of the last layer in `float32` for the same reasons."
      ],
      "id": "bR-_yaEOPsfQ"
    },
    {
      "cell_type": "code",
      "source": [
        "!python -m pip install -U peft"
      ],
      "metadata": {
        "id": "aoAqAR34YJPK",
        "outputId": "f556fff9-ebd8-442d-9768-c244bb112dea",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "aoAqAR34YJPK",
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: peft in /usr/local/lib/python3.10/dist-packages (0.8.2)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from peft) (1.23.5)\n",
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            "Requirement already satisfied: torch>=1.13.0 in /usr/local/lib/python3.10/dist-packages (from peft) (2.1.0+cu121)\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (from peft) (4.38.0.dev0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from peft) (4.66.1)\n",
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            "Requirement already satisfied: safetensors in /usr/local/lib/python3.10/dist-packages (from peft) (0.4.2)\n",
            "Requirement already satisfied: huggingface-hub>=0.17.0 in /usr/local/lib/python3.10/dist-packages (from peft) (0.20.3)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.17.0->peft) (3.13.1)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.17.0->peft) (2023.6.0)\n",
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            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (3.2.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (3.1.3)\n",
            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (2.1.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers->peft) (2023.12.25)\n",
            "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers->peft) (0.15.1)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.13.0->peft) (2.1.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.17.0->peft) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.17.0->peft) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.17.0->peft) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.17.0->peft) (2024.2.2)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.13.0->peft) (1.3.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "Cl_ZQualPt9R"
      },
      "outputs": [],
      "source": [
        "import peft\n",
        "from peft import prepare_model_for_int8_training\n",
        "\n",
        "model = prepare_model_for_int8_training(model)"
      ],
      "id": "Cl_ZQualPt9R"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vjl4j4RJPmPR"
      },
      "source": [
        "### Apply LoRA\n",
        "\n",
        "Here comes the magic with `peft`! Let's load a `PeftModel` and specify that we are going to use low-rank adapters (LoRA) using `get_peft_model` utility function from `peft`."
      ],
      "id": "Vjl4j4RJPmPR"
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DQtpDPRHPyOL",
        "outputId": "2604a09d-67aa-4632-ba43-f65906c391c9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 15,728,640 || all params: 1,559,033,600 || trainable%: 1.0088711365810203\n"
          ]
        }
      ],
      "source": [
        "from peft import LoraConfig, PeftModel, LoraModel, LoraConfig, get_peft_model\n",
        "\n",
        "config = LoraConfig(r=32, lora_alpha=64, target_modules=[\"q_proj\", \"v_proj\"], lora_dropout=0.05, bias=\"none\")\n",
        "\n",
        "model = get_peft_model(model, config)\n",
        "model.print_trainable_parameters()"
      ],
      "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": 29,
      "metadata": {
        "id": "0ae3e9af-97b7-4aa0-ae85-20b23b5bcb3a"
      },
      "outputs": [],
      "source": [
        "from transformers import Seq2SeqTrainingArguments\n",
        "\n",
        "training_args = Seq2SeqTrainingArguments(\n",
        "    output_dir=\"temp\",  # change to a repo name of your choice\n",
        "    per_device_train_batch_size=8,\n",
        "    gradient_accumulation_steps=1,  # increase by 2x for every 2x decrease in batch size\n",
        "    learning_rate=1e-3,\n",
        "    warmup_steps=50,\n",
        "    num_train_epochs=3,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    fp16=True,\n",
        "    per_device_eval_batch_size=8,\n",
        "    generation_max_length=128,\n",
        "    logging_steps=25,\n",
        "    remove_unused_columns=False,  # required as the PeftModel forward doesn't have the signature of the wrapped model's forward\n",
        "    label_names=[\"labels\"],  # same reason as above\n",
        ")"
      ],
      "id": "0ae3e9af-97b7-4aa0-ae85-20b23b5bcb3a"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b3a944d8-3112-4552-82a0-be25988b3857"
      },
      "source": [
        "**Few Important Notes:**\n",
        "1. `remove_unused_columns=False` and `label_names=[\"labels\"]` are required as the PeftModel's forward doesn't have the signature of the base model's forward.\n",
        "\n",
        "2. INT8 training required autocasting. `predict_with_generate` can't be passed to Trainer because it internally calls transformer's `generate` without autocasting leading to errors.\n",
        "\n",
        "3. Because of point 2, `compute_metrics` shouldn't be passed to `Seq2SeqTrainer` as seen below. (commented out)"
      ],
      "id": "b3a944d8-3112-4552-82a0-be25988b3857"
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "d546d7fe-0543-479a-b708-2ebabec19493"
      },
      "outputs": [],
      "source": [
        "from transformers import Seq2SeqTrainer\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",
        ")\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": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "id": "ee8b7b8e-1c9a-4d77-9137-1778a629e6de",
        "outputId": "88e3cb3d-6ead-4622-c170-1408ec331f2e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:61: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:322: 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": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='471' max='1473' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [ 471/1473 50:58 < 1:48:55, 0.15 it/s, Epoch 0.96/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "import time\n",
        "\n",
        "start = time.time()\n",
        "trainer.train()\n",
        "end = time.time()\n",
        "\n",
        "print(f\"PEFT-LoRa Whisper Training Time: {end-time}:.2f\")"
      ],
      "id": "ee8b7b8e-1c9a-4d77-9137-1778a629e6de"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "referenced_widgets": [
            "dd863f41e0484e999b8548ee7af8e058",
            "ed659b121be3438fa5ff7847725a4e52"
          ]
        },
        "id": "0576aa2a",
        "outputId": "af58b2eb-80f4-430c-9968-d169ed03c75a"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Uploading the following files to smangrul/openai-whisper-large-v2-LORA-colab: adapter_model.bin,adapter_config.json\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dd863f41e0484e999b8548ee7af8e058",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Upload 1 LFS files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ed659b121be3438fa5ff7847725a4e52",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "adapter_model.bin:   0%|          | 0.00/63.1M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "smangrul/openai-whisper-large-v2-LORA-colab\n"
          ]
        }
      ],
      "source": [
        "model_name_or_path = \"openai/whisper-large-v2\"\n",
        "peft_model_id = \"smangrul/\" + f\"{model_name_or_path}-{model.peft_config.peft_type}-colab\".replace(\"/\", \"-\")\n",
        "model.push_to_hub(peft_model_id)\n",
        "print(peft_model_id)"
      ],
      "id": "0576aa2a"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SlyyOGnPgi_I"
      },
      "source": [
        "# Evaluation and Inference"
      ],
      "id": "SlyyOGnPgi_I"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kzfg2qoXgrhg"
      },
      "source": [
        "**Important points to note while inferencing**:\n",
        "1. As `predict_with_generate` can't be used, we will write the eval loop with `torch.cuda.amp.autocast()` as shown below.\n",
        "2. As the base model is frozen, PEFT model sometimes fails ot recognise the language while decoding.Hence, we force the starting tokens to mention the language we are transcribing. This is done via `forced_decoder_ids = processor.get_decoder_prompt_ids(language=\"Marathi\", task=\"transcribe\")` and passing that too the `model.generate` call.\n",
        "3. Please note that [AutoEvaluate Leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=mozilla-foundation%2Fcommon_voice_11_0&only_verified=0&task=automatic-speech-recognition&config=mr&split=test&metric=wer) for `mr` language on `common_voice_11_0` has a bug wherein openai's `BasicTextNormalizer` normalizer is used while evaluation leading to degerated output text, an example is shown below:\n",
        "```\n",
        "without normalizer: 'स्विच्चान नरुवित्तीची पद्दत मोठ्या प्रमाणात आमलात आणल्या बसोन या दुपन्याने अनेक राथ प्रवेश केला आहे.'\n",
        "with normalizer: 'स व च च न नर व त त च पद दत म ठ य प रम ण त आमल त आणल य बस न य द पन य न अन क र थ प रव श क ल आह'\n",
        "```\n",
        "Post fixing this bug, we report the 2 metrics for the top model of the leaderboard and the PEFT model:\n",
        "1. `wer`: `wer` without using the `BasicTextNormalizer` as it doesn't cater to most indic languages. This is want we consider as true performance metric.\n",
        "2. `normalized_wer`: `wer` using the `BasicTextNormalizer` to be comparable to the leaderboard metrics.\n",
        "Below are the results:\n",
        "\n",
        "| Model          | DrishtiSharma/whisper-large-v2-marathi | smangrul/openai-whisper-large-v2-LORA-colab |\n",
        "|----------------|----------------------------------------|---------------------------------------------|\n",
        "| wer            | 35.6457                                | 36.1356                                     |\n",
        "| normalized_wer | 13.6440                                | 14.0165                                     |\n",
        "\n",
        "We see that PEFT model's performance is comparable to the fully fine-tuned model on the top of the leaderboard. At the same time, we are able to train the large model in Colab notebook with limited GPU memory and the added advantage of resulting checkpoint being jsut `63` MB.\n",
        "\n"
      ],
      "id": "Kzfg2qoXgrhg"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "referenced_widgets": [
            "36e70a8474654c07b6160c81bb8f71ac",
            "c302ca92974f4b9298d3a6fc66578e60"
          ]
        },
        "id": "273a996c",
        "outputId": "a51fc133-a2c7-4d38-eeca-92bbc9a0a71e"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "36e70a8474654c07b6160c81bb8f71ac",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)/adapter_config.json:   0%|          | 0.00/358 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Overriding torch_dtype=None with `torch_dtype=torch.float16` due to requirements of `bitsandbytes` to enable model loading in mixed int8. Either pass torch_dtype=torch.float16 or don't pass this argument at all to remove this warning.\n",
            "loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--openai--whisper-large-v2/snapshots/bd0efe4d58db161e5ca3940e7c5940221e1b9646/config.json\n",
            "Model config WhisperConfig {\n",
            "  \"_name_or_path\": \"openai/whisper-large-v2\",\n",
            "  \"activation_dropout\": 0.0,\n",
            "  \"activation_function\": \"gelu\",\n",
            "  \"architectures\": [\n",
            "    \"WhisperForConditionalGeneration\"\n",
            "  ],\n",
            "  \"attention_dropout\": 0.0,\n",
            "  \"begin_suppress_tokens\": [\n",
            "    220,\n",
            "    50257\n",
            "  ],\n",
            "  \"bos_token_id\": 50257,\n",
            "  \"d_model\": 1280,\n",
            "  \"decoder_attention_heads\": 20,\n",
            "  \"decoder_ffn_dim\": 5120,\n",
            "  \"decoder_layerdrop\": 0.0,\n",
            "  \"decoder_layers\": 32,\n",
            "  \"decoder_start_token_id\": 50258,\n",
            "  \"dropout\": 0.0,\n",
            "  \"encoder_attention_heads\": 20,\n",
            "  \"encoder_ffn_dim\": 5120,\n",
            "  \"encoder_layerdrop\": 0.0,\n",
            "  \"encoder_layers\": 32,\n",
            "  \"eos_token_id\": 50257,\n",
            "  \"forced_decoder_ids\": [\n",
            "    [\n",
            "      1,\n",
            "      50259\n",
            "    ],\n",
            "    [\n",
            "      2,\n",
            "      50359\n",
            "    ],\n",
            "    [\n",
            "      3,\n",
            "      50363\n",
            "    ]\n",
            "  ],\n",
            "  \"init_std\": 0.02,\n",
            "  \"is_encoder_decoder\": true,\n",
            "  \"max_length\": 448,\n",
            "  \"max_source_positions\": 1500,\n",
            "  \"max_target_positions\": 448,\n",
            "  \"model_type\": \"whisper\",\n",
            "  \"num_hidden_layers\": 32,\n",
            "  \"num_mel_bins\": 80,\n",
            "  \"pad_token_id\": 50257,\n",
            "  \"scale_embedding\": false,\n",
            "  \"suppress_tokens\": [\n",
            "    1,\n",
            "    2,\n",
            "    7,\n",
            "    8,\n",
            "    9,\n",
            "    10,\n",
            "    14,\n",
            "    25,\n",
            "    26,\n",
            "    27,\n",
            "    28,\n",
            "    29,\n",
            "    31,\n",
            "    58,\n",
            "    59,\n",
            "    60,\n",
            "    61,\n",
            "    62,\n",
            "    63,\n",
            "    90,\n",
            "    91,\n",
            "    92,\n",
            "    93,\n",
            "    359,\n",
            "    503,\n",
            "    522,\n",
            "    542,\n",
            "    873,\n",
            "    893,\n",
            "    902,\n",
            "    918,\n",
            "    922,\n",
            "    931,\n",
            "    1350,\n",
            "    1853,\n",
            "    1982,\n",
            "    2460,\n",
            "    2627,\n",
            "    3246,\n",
            "    3253,\n",
            "    3268,\n",
            "    3536,\n",
            "    3846,\n",
            "    3961,\n",
            "    4183,\n",
            "    4667,\n",
            "    6585,\n",
            "    6647,\n",
            "    7273,\n",
            "    9061,\n",
            "    9383,\n",
            "    10428,\n",
            "    10929,\n",
            "    11938,\n",
            "    12033,\n",
            "    12331,\n",
            "    12562,\n",
            "    13793,\n",
            "    14157,\n",
            "    14635,\n",
            "    15265,\n",
            "    15618,\n",
            "    16553,\n",
            "    16604,\n",
            "    18362,\n",
            "    18956,\n",
            "    20075,\n",
            "    21675,\n",
            "    22520,\n",
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            "    26161,\n",
            "    26435,\n",
            "    28279,\n",
            "    29464,\n",
            "    31650,\n",
            "    32302,\n",
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            "    42863,\n",
            "    47425,\n",
            "    49870,\n",
            "    50254,\n",
            "    50258,\n",
            "    50360,\n",
            "    50361,\n",
            "    50362\n",
            "  ],\n",
            "  \"torch_dtype\": \"float32\",\n",
            "  \"transformers_version\": \"4.27.0.dev0\",\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 51865\n",
            "}\n",
            "\n",
            "loading weights file pytorch_model.bin from cache at /root/.cache/huggingface/hub/models--openai--whisper-large-v2/snapshots/bd0efe4d58db161e5ca3940e7c5940221e1b9646/pytorch_model.bin\n",
            "Instantiating WhisperForConditionalGeneration model under default dtype torch.float16.\n",
            "Generate config GenerationConfig {\n",
            "  \"_from_model_config\": true,\n",
            "  \"begin_suppress_tokens\": [\n",
            "    220,\n",
            "    50257\n",
            "  ],\n",
            "  \"bos_token_id\": 50257,\n",
            "  \"decoder_start_token_id\": 50258,\n",
            "  \"eos_token_id\": 50257,\n",
            "  \"forced_decoder_ids\": [\n",
            "    [\n",
            "      1,\n",
            "      50259\n",
            "    ],\n",
            "    [\n",
            "      2,\n",
            "      50359\n",
            "    ],\n",
            "    [\n",
            "      3,\n",
            "      50363\n",
            "    ]\n",
            "  ],\n",
            "  \"max_length\": 448,\n",
            "  \"pad_token_id\": 50257,\n",
            "  \"suppress_tokens\": [\n",
            "    1,\n",
            "    2,\n",
            "    7,\n",
            "    8,\n",
            "    9,\n",
            "    10,\n",
            "    14,\n",
            "    25,\n",
            "    26,\n",
            "    27,\n",
            "    28,\n",
            "    29,\n",
            "    31,\n",
            "    58,\n",
            "    59,\n",
            "    60,\n",
            "    61,\n",
            "    62,\n",
            "    63,\n",
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            "    873,\n",
            "    893,\n",
            "    902,\n",
            "    918,\n",
            "    922,\n",
            "    931,\n",
            "    1350,\n",
            "    1853,\n",
            "    1982,\n",
            "    2460,\n",
            "    2627,\n",
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            "    3253,\n",
            "    3268,\n",
            "    3536,\n",
            "    3846,\n",
            "    3961,\n",
            "    4183,\n",
            "    4667,\n",
            "    6585,\n",
            "    6647,\n",
            "    7273,\n",
            "    9061,\n",
            "    9383,\n",
            "    10428,\n",
            "    10929,\n",
            "    11938,\n",
            "    12033,\n",
            "    12331,\n",
            "    12562,\n",
            "    13793,\n",
            "    14157,\n",
            "    14635,\n",
            "    15265,\n",
            "    15618,\n",
            "    16553,\n",
            "    16604,\n",
            "    18362,\n",
            "    18956,\n",
            "    20075,\n",
            "    21675,\n",
            "    22520,\n",
            "    26130,\n",
            "    26161,\n",
            "    26435,\n",
            "    28279,\n",
            "    29464,\n",
            "    31650,\n",
            "    32302,\n",
            "    32470,\n",
            "    36865,\n",
            "    42863,\n",
            "    47425,\n",
            "    49870,\n",
            "    50254,\n",
            "    50258,\n",
            "    50360,\n",
            "    50361,\n",
            "    50362\n",
            "  ],\n",
            "  \"transformers_version\": \"4.27.0.dev0\"\n",
            "}\n",
            "\n",
            "Detected 8-bit loading: activating 8-bit loading for this model\n",
            "All model checkpoint weights were used when initializing WhisperForConditionalGeneration.\n",
            "\n",
            "All the weights of WhisperForConditionalGeneration were initialized from the model checkpoint at openai/whisper-large-v2.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use WhisperForConditionalGeneration for predictions without further training.\n",
            "loading configuration file generation_config.json from cache at /root/.cache/huggingface/hub/models--openai--whisper-large-v2/snapshots/bd0efe4d58db161e5ca3940e7c5940221e1b9646/generation_config.json\n",
            "Generate config GenerationConfig {\n",
            "  \"begin_suppress_tokens\": [\n",
            "    220,\n",
            "    50257\n",
            "  ],\n",
            "  \"bos_token_id\": 50257,\n",
            "  \"decoder_start_token_id\": 50258,\n",
            "  \"eos_token_id\": 50257,\n",
            "  \"forced_decoder_ids\": [\n",
            "    [\n",
            "      1,\n",
            "      null\n",
            "    ],\n",
            "    [\n",
            "      2,\n",
            "      50359\n",
            "    ]\n",
            "  ],\n",
            "  \"is_multilingual\": true,\n",
            "  \"lang_to_id\": {\n",
            "    \"<|af|>\": 50327,\n",
            "    \"<|am|>\": 50334,\n",
            "    \"<|ar|>\": 50272,\n",
            "    \"<|as|>\": 50350,\n",
            "    \"<|az|>\": 50304,\n",
            "    \"<|ba|>\": 50355,\n",
            "    \"<|be|>\": 50330,\n",
            "    \"<|bg|>\": 50292,\n",
            "    \"<|bn|>\": 50302,\n",
            "    \"<|bo|>\": 50347,\n",
            "    \"<|br|>\": 50309,\n",
            "    \"<|bs|>\": 50315,\n",
            "    \"<|ca|>\": 50270,\n",
            "    \"<|cs|>\": 50283,\n",
            "    \"<|cy|>\": 50297,\n",
            "    \"<|da|>\": 50285,\n",
            "    \"<|de|>\": 50261,\n",
            "    \"<|el|>\": 50281,\n",
            "    \"<|en|>\": 50259,\n",
            "    \"<|es|>\": 50262,\n",
            "    \"<|et|>\": 50307,\n",
            "    \"<|eu|>\": 50310,\n",
            "    \"<|fa|>\": 50300,\n",
            "    \"<|fi|>\": 50277,\n",
            "    \"<|fo|>\": 50338,\n",
            "    \"<|fr|>\": 50265,\n",
            "    \"<|gl|>\": 50319,\n",
            "    \"<|gu|>\": 50333,\n",
            "    \"<|haw|>\": 50352,\n",
            "    \"<|ha|>\": 50354,\n",
            "    \"<|he|>\": 50279,\n",
            "    \"<|hi|>\": 50276,\n",
            "    \"<|hr|>\": 50291,\n",
            "    \"<|ht|>\": 50339,\n",
            "    \"<|hu|>\": 50286,\n",
            "    \"<|hy|>\": 50312,\n",
            "    \"<|id|>\": 50275,\n",
            "    \"<|is|>\": 50311,\n",
            "    \"<|it|>\": 50274,\n",
            "    \"<|ja|>\": 50266,\n",
            "    \"<|jw|>\": 50356,\n",
            "    \"<|ka|>\": 50329,\n",
            "    \"<|kk|>\": 50316,\n",
            "    \"<|km|>\": 50323,\n",
            "    \"<|kn|>\": 50306,\n",
            "    \"<|ko|>\": 50264,\n",
            "    \"<|la|>\": 50294,\n",
            "    \"<|lb|>\": 50345,\n",
            "    \"<|ln|>\": 50353,\n",
            "    \"<|lo|>\": 50336,\n",
            "    \"<|lt|>\": 50293,\n",
            "    \"<|lv|>\": 50301,\n",
            "    \"<|mg|>\": 50349,\n",
            "    \"<|mi|>\": 50295,\n",
            "    \"<|mk|>\": 50308,\n",
            "    \"<|ml|>\": 50296,\n",
            "    \"<|mn|>\": 50314,\n",
            "    \"<|mr|>\": 50320,\n",
            "    \"<|ms|>\": 50282,\n",
            "    \"<|mt|>\": 50343,\n",
            "    \"<|my|>\": 50346,\n",
            "    \"<|ne|>\": 50313,\n",
            "    \"<|nl|>\": 50271,\n",
            "    \"<|nn|>\": 50342,\n",
            "    \"<|no|>\": 50288,\n",
            "    \"<|oc|>\": 50328,\n",
            "    \"<|pa|>\": 50321,\n",
            "    \"<|pl|>\": 50269,\n",
            "    \"<|ps|>\": 50340,\n",
            "    \"<|pt|>\": 50267,\n",
            "    \"<|ro|>\": 50284,\n",
            "    \"<|ru|>\": 50263,\n",
            "    \"<|sa|>\": 50344,\n",
            "    \"<|sd|>\": 50332,\n",
            "    \"<|si|>\": 50322,\n",
            "    \"<|sk|>\": 50298,\n",
            "    \"<|sl|>\": 50305,\n",
            "    \"<|sn|>\": 50324,\n",
            "    \"<|so|>\": 50326,\n",
            "    \"<|sq|>\": 50317,\n",
            "    \"<|sr|>\": 50303,\n",
            "    \"<|su|>\": 50357,\n",
            "    \"<|sv|>\": 50273,\n",
            "    \"<|sw|>\": 50318,\n",
            "    \"<|ta|>\": 50287,\n",
            "    \"<|te|>\": 50299,\n",
            "    \"<|tg|>\": 50331,\n",
            "    \"<|th|>\": 50289,\n",
            "    \"<|tk|>\": 50341,\n",
            "    \"<|tl|>\": 50348,\n",
            "    \"<|tr|>\": 50268,\n",
            "    \"<|tt|>\": 50351,\n",
            "    \"<|uk|>\": 50280,\n",
            "    \"<|ur|>\": 50290,\n",
            "    \"<|uz|>\": 50337,\n",
            "    \"<|vi|>\": 50278,\n",
            "    \"<|yi|>\": 50335,\n",
            "    \"<|yo|>\": 50325,\n",
            "    \"<|zh|>\": 50260\n",
            "  },\n",
            "  \"max_initial_timestamp_index\": 1,\n",
            "  \"max_length\": 448,\n",
            "  \"no_timestamps_token_id\": 50363,\n",
            "  \"pad_token_id\": 50256,\n",
            "  \"return_timestamps\": false,\n",
            "  \"suppress_tokens\": [\n",
            "    1,\n",
            "    2,\n",
            "    7,\n",
            "    8,\n",
            "    9,\n",
            "    10,\n",
            "    14,\n",
            "    25,\n",
            "    26,\n",
            "    27,\n",
            "    28,\n",
            "    29,\n",
            "    31,\n",
            "    58,\n",
            "    59,\n",
            "    60,\n",
            "    61,\n",
            "    62,\n",
            "    63,\n",
            "    90,\n",
            "    91,\n",
            "    92,\n",
            "    93,\n",
            "    359,\n",
            "    503,\n",
            "    522,\n",
            "    542,\n",
            "    873,\n",
            "    893,\n",
            "    902,\n",
            "    918,\n",
            "    922,\n",
            "    931,\n",
            "    1350,\n",
            "    1853,\n",
            "    1982,\n",
            "    2460,\n",
            "    2627,\n",
            "    3246,\n",
            "    3253,\n",
            "    3268,\n",
            "    3536,\n",
            "    3846,\n",
            "    3961,\n",
            "    4183,\n",
            "    4667,\n",
            "    6585,\n",
            "    6647,\n",
            "    7273,\n",
            "    9061,\n",
            "    9383,\n",
            "    10428,\n",
            "    10929,\n",
            "    11938,\n",
            "    12033,\n",
            "    12331,\n",
            "    12562,\n",
            "    13793,\n",
            "    14157,\n",
            "    14635,\n",
            "    15265,\n",
            "    15618,\n",
            "    16553,\n",
            "    16604,\n",
            "    18362,\n",
            "    18956,\n",
            "    20075,\n",
            "    21675,\n",
            "    22520,\n",
            "    26130,\n",
            "    26161,\n",
            "    26435,\n",
            "    28279,\n",
            "    29464,\n",
            "    31650,\n",
            "    32302,\n",
            "    32470,\n",
            "    36865,\n",
            "    42863,\n",
            "    47425,\n",
            "    49870,\n",
            "    50254,\n",
            "    50258,\n",
            "    50360,\n",
            "    50361,\n",
            "    50362\n",
            "  ],\n",
            "  \"task_to_id\": {\n",
            "    \"transcribe\": 50359,\n",
            "    \"translate\": 50358\n",
            "  },\n",
            "  \"transformers_version\": \"4.27.0.dev0\"\n",
            "}\n",
            "\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c302ca92974f4b9298d3a6fc66578e60",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)\"adapter_model.bin\";:   0%|          | 0.00/63.1M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from peft import PeftModel, PeftConfig\n",
        "from transformers import WhisperForConditionalGeneration, Seq2SeqTrainer\n",
        "\n",
        "peft_model_id = \"smangrul/openai-whisper-large-v2-LORA-colab\"\n",
        "peft_config = PeftConfig.from_pretrained(peft_model_id)\n",
        "model = WhisperForConditionalGeneration.from_pretrained(\n",
        "    peft_config.base_model_name_or_path, load_in_8bit=True, device_map=\"auto\"\n",
        ")\n",
        "model = PeftModel.from_pretrained(model, peft_model_id)"
      ],
      "id": "273a996c"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "401ceaa6",
        "outputId": "a8feacbf-ffdf-4104-96a6-096c559985dc"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 227/227 [1:48:29<00:00, 28.68s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "wer=36.74811424150603\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "from torch.utils.data import DataLoader\n",
        "from tqdm import tqdm\n",
        "import numpy as np\n",
        "import gc\n",
        "\n",
        "eval_dataloader = DataLoader(common_voice[\"test\"], batch_size=8, collate_fn=data_collator)\n",
        "\n",
        "model.eval()\n",
        "for step, batch in enumerate(tqdm(eval_dataloader)):\n",
        "    with torch.cuda.amp.autocast():\n",
        "        with torch.no_grad():\n",
        "            generated_tokens = (\n",
        "                model.generate(\n",
        "                    input_features=batch[\"input_features\"].to(\"cuda\"),\n",
        "                    decoder_input_ids=batch[\"labels\"][:, :4].to(\"cuda\"),\n",
        "                    max_new_tokens=255,\n",
        "                )\n",
        "                .cpu()\n",
        "                .numpy()\n",
        "            )\n",
        "            labels = batch[\"labels\"].cpu().numpy()\n",
        "            labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
        "            decoded_preds = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)\n",
        "            decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
        "            metric.add_batch(\n",
        "                predictions=decoded_preds,\n",
        "                references=decoded_labels,\n",
        "            )\n",
        "    del generated_tokens, labels, batch\n",
        "    gc.collect()\n",
        "wer = 100 * metric.compute()\n",
        "print(f\"{wer=}\")"
      ],
      "id": "401ceaa6"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8ZN4pTangw98"
      },
      "source": [
        "## Using AutomaticSpeechRecognitionPipeline"
      ],
      "id": "8ZN4pTangw98"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hpDx1AOCgwuk"
      },
      "source": [
        "**Few important notes:**\n",
        "1. `pipe()` should be in the autocast context manager `with torch.cuda.amp.autocast():`\n",
        "2. `forced_decoder_ids` specifying the `language` being transcribed should be provided in `generate_kwargs` dict.\n",
        "3. You will get warning along the below lines which is **safe to ignore**.\n",
        "```\n",
        "The model 'PeftModel' is not supported for . Supported models are ['SpeechEncoderDecoderModel', 'Speech2TextForConditionalGeneration', 'SpeechT5ForSpeechToText', 'WhisperForConditionalGeneration', 'Data2VecAudioForCTC', 'HubertForCTC', 'MCTCTForCTC', 'SEWForCTC', 'SEWDForCTC', 'UniSpeechForCTC', 'UniSpeechSatForCTC', 'Wav2Vec2ForCTC', 'Wav2Vec2ConformerForCTC', 'WavLMForCTC'].\n",
        "\n",
        "```"
      ],
      "id": "hpDx1AOCgwuk"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "fa2b62a3",
        "outputId": "513a2c4b-5551-4991-fafb-bac51884724d"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Overriding torch_dtype=None with `torch_dtype=torch.float16` due to requirements of `bitsandbytes` to enable model loading in mixed int8. Either pass torch_dtype=torch.float16 or don't pass this argument at all to remove this warning.\n",
            "loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--openai--whisper-large-v2/snapshots/bd0efe4d58db161e5ca3940e7c5940221e1b9646/config.json\n",
            "Model config WhisperConfig {\n",
            "  \"_name_or_path\": \"openai/whisper-large-v2\",\n",
            "  \"activation_dropout\": 0.0,\n",
            "  \"activation_function\": \"gelu\",\n",
            "  \"architectures\": [\n",
            "    \"WhisperForConditionalGeneration\"\n",
            "  ],\n",
            "  \"attention_dropout\": 0.0,\n",
            "  \"begin_suppress_tokens\": [\n",
            "    220,\n",
            "    50257\n",
            "  ],\n",
            "  \"bos_token_id\": 50257,\n",
            "  \"d_model\": 1280,\n",
            "  \"decoder_attention_heads\": 20,\n",
            "  \"decoder_ffn_dim\": 5120,\n",
            "  \"decoder_layerdrop\": 0.0,\n",
            "  \"decoder_layers\": 32,\n",
            "  \"decoder_start_token_id\": 50258,\n",
            "  \"dropout\": 0.0,\n",
            "  \"encoder_attention_heads\": 20,\n",
            "  \"encoder_ffn_dim\": 5120,\n",
            "  \"encoder_layerdrop\": 0.0,\n",
            "  \"encoder_layers\": 32,\n",
            "  \"eos_token_id\": 50257,\n",
            "  \"forced_decoder_ids\": [\n",
            "    [\n",
            "      1,\n",
            "      50259\n",
            "    ],\n",
            "    [\n",
            "      2,\n",
            "      50359\n",
            "    ],\n",
            "    [\n",
            "      3,\n",
            "      50363\n",
            "    ]\n",
            "  ],\n",
            "  \"init_std\": 0.02,\n",
            "  \"is_encoder_decoder\": true,\n",
            "  \"max_length\": 448,\n",
            "  \"max_source_positions\": 1500,\n",
            "  \"max_target_positions\": 448,\n",
            "  \"model_type\": \"whisper\",\n",
            "  \"num_hidden_layers\": 32,\n",
            "  \"num_mel_bins\": 80,\n",
            "  \"pad_token_id\": 50257,\n",
            "  \"scale_embedding\": false,\n",
            "  \"suppress_tokens\": [\n",
            "    1,\n",
            "    2,\n",
            "    7,\n",
            "    8,\n",
            "    9,\n",
            "    10,\n",
            "    14,\n",
            "    25,\n",
            "    26,\n",
            "    27,\n",
            "    28,\n",
            "    29,\n",
            "    31,\n",
            "    58,\n",
            "    59,\n",
            "    60,\n",
            "    61,\n",
            "    62,\n",
            "    63,\n",
            "    90,\n",
            "    91,\n",
            "    92,\n",
            "    93,\n",
            "    359,\n",
            "    503,\n",
            "    522,\n",
            "    542,\n",
            "    873,\n",
            "    893,\n",
            "    902,\n",
            "    918,\n",
            "    922,\n",
            "    931,\n",
            "    1350,\n",
            "    1853,\n",
            "    1982,\n",
            "    2460,\n",
            "    2627,\n",
            "    3246,\n",
            "    3253,\n",
            "    3268,\n",
            "    3536,\n",
            "    3846,\n",
            "    3961,\n",
            "    4183,\n",
            "    4667,\n",
            "    6585,\n",
            "    6647,\n",
            "    7273,\n",
            "    9061,\n",
            "    9383,\n",
            "    10428,\n",
            "    10929,\n",
            "    11938,\n",
            "    12033,\n",
            "    12331,\n",
            "    12562,\n",
            "    13793,\n",
            "    14157,\n",
            "    14635,\n",
            "    15265,\n",
            "    15618,\n",
            "    16553,\n",
            "    16604,\n",
            "    18362,\n",
            "    18956,\n",
            "    20075,\n",
            "    21675,\n",
            "    22520,\n",
            "    26130,\n",
            "    26161,\n",
            "    26435,\n",
            "    28279,\n",
            "    29464,\n",
            "    31650,\n",
            "    32302,\n",
            "    32470,\n",
            "    36865,\n",
            "    42863,\n",
            "    47425,\n",
            "    49870,\n",
            "    50254,\n",
            "    50258,\n",
            "    50360,\n",
            "    50361,\n",
            "    50362\n",
            "  ],\n",
            "  \"torch_dtype\": \"float32\",\n",
            "  \"transformers_version\": \"4.27.0.dev0\",\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 51865\n",
            "}\n",
            "\n",
            "loading weights file pytorch_model.bin from cache at /root/.cache/huggingface/hub/models--openai--whisper-large-v2/snapshots/bd0efe4d58db161e5ca3940e7c5940221e1b9646/pytorch_model.bin\n",
            "Instantiating WhisperForConditionalGeneration model under default dtype torch.float16.\n",
            "Generate config GenerationConfig {\n",
            "  \"_from_model_config\": true,\n",
            "  \"begin_suppress_tokens\": [\n",
            "    220,\n",
            "    50257\n",
            "  ],\n",
            "  \"bos_token_id\": 50257,\n",
            "  \"decoder_start_token_id\": 50258,\n",
            "  \"eos_token_id\": 50257,\n",
            "  \"forced_decoder_ids\": [\n",
            "    [\n",
            "      1,\n",
            "      50259\n",
            "    ],\n",
            "    [\n",
            "      2,\n",
            "      50359\n",
            "    ],\n",
            "    [\n",
            "      3,\n",
            "      50363\n",
            "    ]\n",
            "  ],\n",
            "  \"max_length\": 448,\n",
            "  \"pad_token_id\": 50257,\n",
            "  \"suppress_tokens\": [\n",
            "    1,\n",
            "    2,\n",
            "    7,\n",
            "    8,\n",
            "    9,\n",
            "    10,\n",
            "    14,\n",
            "    25,\n",
            "    26,\n",
            "    27,\n",
            "    28,\n",
            "    29,\n",
            "    31,\n",
            "    58,\n",
            "    59,\n",
            "    60,\n",
            "    61,\n",
            "    62,\n",
            "    63,\n",
            "    90,\n",
            "    91,\n",
            "    92,\n",
            "    93,\n",
            "    359,\n",
            "    503,\n",
            "    522,\n",
            "    542,\n",
            "    873,\n",
            "    893,\n",
            "    902,\n",
            "    918,\n",
            "    922,\n",
            "    931,\n",
            "    1350,\n",
            "    1853,\n",
            "    1982,\n",
            "    2460,\n",
            "    2627,\n",
            "    3246,\n",
            "    3253,\n",
            "    3268,\n",
            "    3536,\n",
            "    3846,\n",
            "    3961,\n",
            "    4183,\n",
            "    4667,\n",
            "    6585,\n",
            "    6647,\n",
            "    7273,\n",
            "    9061,\n",
            "    9383,\n",
            "    10428,\n",
            "    10929,\n",
            "    11938,\n",
            "    12033,\n",
            "    12331,\n",
            "    12562,\n",
            "    13793,\n",
            "    14157,\n",
            "    14635,\n",
            "    15265,\n",
            "    15618,\n",
            "    16553,\n",
            "    16604,\n",
            "    18362,\n",
            "    18956,\n",
            "    20075,\n",
            "    21675,\n",
            "    22520,\n",
            "    26130,\n",
            "    26161,\n",
            "    26435,\n",
            "    28279,\n",
            "    29464,\n",
            "    31650,\n",
            "    32302,\n",
            "    32470,\n",
            "    36865,\n",
            "    42863,\n",
            "    47425,\n",
            "    49870,\n",
            "    50254,\n",
            "    50258,\n",
            "    50360,\n",
            "    50361,\n",
            "    50362\n",
            "  ],\n",
            "  \"transformers_version\": \"4.27.0.dev0\"\n",
            "}\n",
            "\n",
            "Detected 8-bit loading: activating 8-bit loading for this model\n",
            "All model checkpoint weights were used when initializing WhisperForConditionalGeneration.\n",
            "\n",
            "All the weights of WhisperForConditionalGeneration were initialized from the model checkpoint at openai/whisper-large-v2.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use WhisperForConditionalGeneration for predictions without further training.\n",
            "loading configuration file generation_config.json from cache at /root/.cache/huggingface/hub/models--openai--whisper-large-v2/snapshots/bd0efe4d58db161e5ca3940e7c5940221e1b9646/generation_config.json\n",
            "Generate config GenerationConfig {\n",
            "  \"begin_suppress_tokens\": [\n",
            "    220,\n",
            "    50257\n",
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            "  \"bos_token_id\": 50257,\n",
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            "  \"eos_token_id\": 50257,\n",
            "  \"forced_decoder_ids\": [\n",
            "    [\n",
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            "      50359\n",
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            "  \"is_multilingual\": true,\n",
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            "    \"<|af|>\": 50327,\n",
            "    \"<|am|>\": 50334,\n",
            "    \"<|ar|>\": 50272,\n",
            "    \"<|as|>\": 50350,\n",
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            "    \"<|ba|>\": 50355,\n",
            "    \"<|be|>\": 50330,\n",
            "    \"<|bg|>\": 50292,\n",
            "    \"<|bn|>\": 50302,\n",
            "    \"<|bo|>\": 50347,\n",
            "    \"<|br|>\": 50309,\n",
            "    \"<|bs|>\": 50315,\n",
            "    \"<|ca|>\": 50270,\n",
            "    \"<|cs|>\": 50283,\n",
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            "    \"<|da|>\": 50285,\n",
            "    \"<|de|>\": 50261,\n",
            "    \"<|el|>\": 50281,\n",
            "    \"<|en|>\": 50259,\n",
            "    \"<|es|>\": 50262,\n",
            "    \"<|et|>\": 50307,\n",
            "    \"<|eu|>\": 50310,\n",
            "    \"<|fa|>\": 50300,\n",
            "    \"<|fi|>\": 50277,\n",
            "    \"<|fo|>\": 50338,\n",
            "    \"<|fr|>\": 50265,\n",
            "    \"<|gl|>\": 50319,\n",
            "    \"<|gu|>\": 50333,\n",
            "    \"<|haw|>\": 50352,\n",
            "    \"<|ha|>\": 50354,\n",
            "    \"<|he|>\": 50279,\n",
            "    \"<|hi|>\": 50276,\n",
            "    \"<|hr|>\": 50291,\n",
            "    \"<|ht|>\": 50339,\n",
            "    \"<|hu|>\": 50286,\n",
            "    \"<|hy|>\": 50312,\n",
            "    \"<|id|>\": 50275,\n",
            "    \"<|is|>\": 50311,\n",
            "    \"<|it|>\": 50274,\n",
            "    \"<|ja|>\": 50266,\n",
            "    \"<|jw|>\": 50356,\n",
            "    \"<|ka|>\": 50329,\n",
            "    \"<|kk|>\": 50316,\n",
            "    \"<|km|>\": 50323,\n",
            "    \"<|kn|>\": 50306,\n",
            "    \"<|ko|>\": 50264,\n",
            "    \"<|la|>\": 50294,\n",
            "    \"<|lb|>\": 50345,\n",
            "    \"<|ln|>\": 50353,\n",
            "    \"<|lo|>\": 50336,\n",
            "    \"<|lt|>\": 50293,\n",
            "    \"<|lv|>\": 50301,\n",
            "    \"<|mg|>\": 50349,\n",
            "    \"<|mi|>\": 50295,\n",
            "    \"<|mk|>\": 50308,\n",
            "    \"<|ml|>\": 50296,\n",
            "    \"<|mn|>\": 50314,\n",
            "    \"<|mr|>\": 50320,\n",
            "    \"<|ms|>\": 50282,\n",
            "    \"<|mt|>\": 50343,\n",
            "    \"<|my|>\": 50346,\n",
            "    \"<|ne|>\": 50313,\n",
            "    \"<|nl|>\": 50271,\n",
            "    \"<|nn|>\": 50342,\n",
            "    \"<|no|>\": 50288,\n",
            "    \"<|oc|>\": 50328,\n",
            "    \"<|pa|>\": 50321,\n",
            "    \"<|pl|>\": 50269,\n",
            "    \"<|ps|>\": 50340,\n",
            "    \"<|pt|>\": 50267,\n",
            "    \"<|ro|>\": 50284,\n",
            "    \"<|ru|>\": 50263,\n",
            "    \"<|sa|>\": 50344,\n",
            "    \"<|sd|>\": 50332,\n",
            "    \"<|si|>\": 50322,\n",
            "    \"<|sk|>\": 50298,\n",
            "    \"<|sl|>\": 50305,\n",
            "    \"<|sn|>\": 50324,\n",
            "    \"<|so|>\": 50326,\n",
            "    \"<|sq|>\": 50317,\n",
            "    \"<|sr|>\": 50303,\n",
            "    \"<|su|>\": 50357,\n",
            "    \"<|sv|>\": 50273,\n",
            "    \"<|sw|>\": 50318,\n",
            "    \"<|ta|>\": 50287,\n",
            "    \"<|te|>\": 50299,\n",
            "    \"<|tg|>\": 50331,\n",
            "    \"<|th|>\": 50289,\n",
            "    \"<|tk|>\": 50341,\n",
            "    \"<|tl|>\": 50348,\n",
            "    \"<|tr|>\": 50268,\n",
            "    \"<|tt|>\": 50351,\n",
            "    \"<|uk|>\": 50280,\n",
            "    \"<|ur|>\": 50290,\n",
            "    \"<|uz|>\": 50337,\n",
            "    \"<|vi|>\": 50278,\n",
            "    \"<|yi|>\": 50335,\n",
            "    \"<|yo|>\": 50325,\n",
            "    \"<|zh|>\": 50260\n",
            "  },\n",
            "  \"max_initial_timestamp_index\": 1,\n",
            "  \"max_length\": 448,\n",
            "  \"no_timestamps_token_id\": 50363,\n",
            "  \"pad_token_id\": 50256,\n",
            "  \"return_timestamps\": false,\n",
            "  \"suppress_tokens\": [\n",
            "    1,\n",
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            "  ],\n",
            "  \"task_to_id\": {\n",
            "    \"transcribe\": 50359,\n",
            "    \"translate\": 50358\n",
            "  },\n",
            "  \"transformers_version\": \"4.27.0.dev0\"\n",
            "}\n",
            "\n",
            "loading file vocab.json from cache at /root/.cache/huggingface/hub/models--openai--whisper-large-v2/snapshots/bd0efe4d58db161e5ca3940e7c5940221e1b9646/vocab.json\n",
            "loading file tokenizer.json from cache at None\n",
            "loading file merges.txt from cache at /root/.cache/huggingface/hub/models--openai--whisper-large-v2/snapshots/bd0efe4d58db161e5ca3940e7c5940221e1b9646/merges.txt\n",
            "loading file normalizer.json from cache at /root/.cache/huggingface/hub/models--openai--whisper-large-v2/snapshots/bd0efe4d58db161e5ca3940e7c5940221e1b9646/normalizer.json\n",
            "loading file added_tokens.json from cache at /root/.cache/huggingface/hub/models--openai--whisper-large-v2/snapshots/bd0efe4d58db161e5ca3940e7c5940221e1b9646/added_tokens.json\n",
            "loading file special_tokens_map.json from cache at /root/.cache/huggingface/hub/models--openai--whisper-large-v2/snapshots/bd0efe4d58db161e5ca3940e7c5940221e1b9646/special_tokens_map.json\n",
            "loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--openai--whisper-large-v2/snapshots/bd0efe4d58db161e5ca3940e7c5940221e1b9646/tokenizer_config.json\n",
            "Adding <|endoftext|> to the vocabulary\n",
            "Adding <|startoftranscript|> to the vocabulary\n",
            "Adding <|en|> to the vocabulary\n",
            "Adding <|zh|> to the vocabulary\n",
            "Adding <|de|> to the vocabulary\n",
            "Adding <|es|> to the vocabulary\n",
            "Adding <|ru|> to the vocabulary\n",
            "Adding <|ko|> to the vocabulary\n",
            "Adding <|fr|> to the vocabulary\n",
            "Adding <|ja|> to the vocabulary\n",
            "Adding <|pt|> to the vocabulary\n",
            "Adding <|tr|> to the vocabulary\n",
            "Adding <|pl|> to the vocabulary\n",
            "Adding <|ca|> to the vocabulary\n",
            "Adding <|nl|> to the vocabulary\n",
            "Adding <|ar|> to the vocabulary\n",
            "Adding <|sv|> to the vocabulary\n",
            "Adding <|it|> to the vocabulary\n",
            "Adding <|id|> to the vocabulary\n",
            "Adding <|hi|> to the vocabulary\n",
            "Adding <|fi|> to the vocabulary\n",
            "Adding <|vi|> to the vocabulary\n",
            "Adding <|he|> to the vocabulary\n",
            "Adding <|uk|> to the vocabulary\n",
            "Adding <|el|> to the vocabulary\n",
            "Adding <|ms|> to the vocabulary\n",
            "Adding <|cs|> to the vocabulary\n",
            "Adding <|ro|> to the vocabulary\n",
            "Adding <|da|> to the vocabulary\n",
            "Adding <|hu|> to the vocabulary\n",
            "Adding <|ta|> to the vocabulary\n",
            "Adding <|no|> to the vocabulary\n",
            "Adding <|th|> to the vocabulary\n",
            "Adding <|ur|> to the vocabulary\n",
            "Adding <|hr|> to the vocabulary\n",
            "Adding <|bg|> to the vocabulary\n",
            "Adding <|lt|> to the vocabulary\n",
            "Adding <|la|> to the vocabulary\n",
            "Adding <|mi|> to the vocabulary\n",
            "Adding <|ml|> to the vocabulary\n",
            "Adding <|cy|> to the vocabulary\n",
            "Adding <|sk|> to the vocabulary\n",
            "Adding <|te|> to the vocabulary\n",
            "Adding <|fa|> to the vocabulary\n",
            "Adding <|lv|> to the vocabulary\n",
            "Adding <|bn|> to the vocabulary\n",
            "Adding <|sr|> to the vocabulary\n",
            "Adding <|az|> to the vocabulary\n",
            "Adding <|sl|> to the vocabulary\n",
            "Adding <|kn|> to the vocabulary\n",
            "Adding <|et|> to the vocabulary\n",
            "Adding <|mk|> to the vocabulary\n",
            "Adding <|br|> to the vocabulary\n",
            "Adding <|eu|> to the vocabulary\n",
            "Adding <|is|> to the vocabulary\n",
            "Adding <|hy|> to the vocabulary\n",
            "Adding <|ne|> to the vocabulary\n",
            "Adding <|mn|> to the vocabulary\n",
            "Adding <|bs|> to the vocabulary\n",
            "Adding <|kk|> to the vocabulary\n",
            "Adding <|sq|> to the vocabulary\n",
            "Adding <|sw|> to the vocabulary\n",
            "Adding <|gl|> to the vocabulary\n",
            "Adding <|mr|> to the vocabulary\n",
            "Adding <|pa|> to the vocabulary\n",
            "Adding <|si|> to the vocabulary\n",
            "Adding <|km|> to the vocabulary\n",
            "Adding <|sn|> to the vocabulary\n",
            "Adding <|yo|> to the vocabulary\n",
            "Adding <|so|> to the vocabulary\n",
            "Adding <|af|> to the vocabulary\n",
            "Adding <|oc|> to the vocabulary\n",
            "Adding <|ka|> to the vocabulary\n",
            "Adding <|be|> to the vocabulary\n",
            "Adding <|tg|> to the vocabulary\n",
            "Adding <|sd|> to the vocabulary\n",
            "Adding <|gu|> to the vocabulary\n",
            "Adding <|am|> to the vocabulary\n",
            "Adding <|yi|> to the vocabulary\n",
            "Adding <|lo|> to the vocabulary\n",
            "Adding <|uz|> to the vocabulary\n",
            "Adding <|fo|> to the vocabulary\n",
            "Adding <|ht|> to the vocabulary\n",
            "Adding <|ps|> to the vocabulary\n",
            "Adding <|tk|> to the vocabulary\n",
            "Adding <|nn|> to the vocabulary\n",
            "Adding <|mt|> to the vocabulary\n",
            "Adding <|sa|> to the vocabulary\n",
            "Adding <|lb|> to the vocabulary\n",
            "Adding <|my|> to the vocabulary\n",
            "Adding <|bo|> to the vocabulary\n",
            "Adding <|tl|> to the vocabulary\n",
            "Adding <|mg|> to the vocabulary\n",
            "Adding <|as|> to the vocabulary\n",
            "Adding <|tt|> to the vocabulary\n",
            "Adding <|haw|> to the vocabulary\n",
            "Adding <|ln|> to the vocabulary\n",
            "Adding <|ha|> to the vocabulary\n",
            "Adding <|ba|> to the vocabulary\n",
            "Adding <|jw|> to the vocabulary\n",
            "Adding <|su|> to the vocabulary\n",
            "Adding <|translate|> to the vocabulary\n",
            "Adding <|transcribe|> to the vocabulary\n",
            "Adding <|startoflm|> to the vocabulary\n",
            "Adding <|startofprev|> to the vocabulary\n",
            "Adding <|nocaptions|> to the vocabulary\n",
            "Adding <|notimestamps|> to the vocabulary\n",
            "loading configuration file preprocessor_config.json from cache at /root/.cache/huggingface/hub/models--openai--whisper-large-v2/snapshots/bd0efe4d58db161e5ca3940e7c5940221e1b9646/preprocessor_config.json\n",
            "Feature extractor WhisperFeatureExtractor {\n",
            "  \"chunk_length\": 30,\n",
            "  \"feature_extractor_type\": \"WhisperFeatureExtractor\",\n",
            "  \"feature_size\": 80,\n",
            "  \"hop_length\": 160,\n",
            "  \"n_fft\": 400,\n",
            "  \"n_samples\": 480000,\n",
            "  \"nb_max_frames\": 3000,\n",
            "  \"padding_side\": \"right\",\n",
            "  \"padding_value\": 0.0,\n",
            "  \"processor_class\": \"WhisperProcessor\",\n",
            "  \"return_attention_mask\": false,\n",
            "  \"sampling_rate\": 16000\n",
            "}\n",
            "\n",
            "loading file vocab.json from cache at /root/.cache/huggingface/hub/models--openai--whisper-large-v2/snapshots/bd0efe4d58db161e5ca3940e7c5940221e1b9646/vocab.json\n",
            "loading file tokenizer.json from cache at None\n",
            "loading file merges.txt from cache at /root/.cache/huggingface/hub/models--openai--whisper-large-v2/snapshots/bd0efe4d58db161e5ca3940e7c5940221e1b9646/merges.txt\n",
            "loading file normalizer.json from cache at /root/.cache/huggingface/hub/models--openai--whisper-large-v2/snapshots/bd0efe4d58db161e5ca3940e7c5940221e1b9646/normalizer.json\n",
            "loading file added_tokens.json from cache at /root/.cache/huggingface/hub/models--openai--whisper-large-v2/snapshots/bd0efe4d58db161e5ca3940e7c5940221e1b9646/added_tokens.json\n",
            "loading file special_tokens_map.json from cache at /root/.cache/huggingface/hub/models--openai--whisper-large-v2/snapshots/bd0efe4d58db161e5ca3940e7c5940221e1b9646/special_tokens_map.json\n",
            "loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--openai--whisper-large-v2/snapshots/bd0efe4d58db161e5ca3940e7c5940221e1b9646/tokenizer_config.json\n",
            "Adding <|endoftext|> to the vocabulary\n",
            "Adding <|startoftranscript|> to the vocabulary\n",
            "Adding <|en|> to the vocabulary\n",
            "Adding <|zh|> to the vocabulary\n",
            "Adding <|de|> to the vocabulary\n",
            "Adding <|es|> to the vocabulary\n",
            "Adding <|ru|> to the vocabulary\n",
            "Adding <|ko|> to the vocabulary\n",
            "Adding <|fr|> to the vocabulary\n",
            "Adding <|ja|> to the vocabulary\n",
            "Adding <|pt|> to the vocabulary\n",
            "Adding <|tr|> to the vocabulary\n",
            "Adding <|pl|> to the vocabulary\n",
            "Adding <|ca|> to the vocabulary\n",
            "Adding <|nl|> to the vocabulary\n",
            "Adding <|ar|> to the vocabulary\n",
            "Adding <|sv|> to the vocabulary\n",
            "Adding <|it|> to the vocabulary\n",
            "Adding <|id|> to the vocabulary\n",
            "Adding <|hi|> to the vocabulary\n",
            "Adding <|fi|> to the vocabulary\n",
            "Adding <|vi|> to the vocabulary\n",
            "Adding <|he|> to the vocabulary\n",
            "Adding <|uk|> to the vocabulary\n",
            "Adding <|el|> to the vocabulary\n",
            "Adding <|ms|> to the vocabulary\n",
            "Adding <|cs|> to the vocabulary\n",
            "Adding <|ro|> to the vocabulary\n",
            "Adding <|da|> to the vocabulary\n",
            "Adding <|hu|> to the vocabulary\n",
            "Adding <|ta|> to the vocabulary\n",
            "Adding <|no|> to the vocabulary\n",
            "Adding <|th|> to the vocabulary\n",
            "Adding <|ur|> to the vocabulary\n",
            "Adding <|hr|> to the vocabulary\n",
            "Adding <|bg|> to the vocabulary\n",
            "Adding <|lt|> to the vocabulary\n",
            "Adding <|la|> to the vocabulary\n",
            "Adding <|mi|> to the vocabulary\n",
            "Adding <|ml|> to the vocabulary\n",
            "Adding <|cy|> to the vocabulary\n",
            "Adding <|sk|> to the vocabulary\n",
            "Adding <|te|> to the vocabulary\n",
            "Adding <|fa|> to the vocabulary\n",
            "Adding <|lv|> to the vocabulary\n",
            "Adding <|bn|> to the vocabulary\n",
            "Adding <|sr|> to the vocabulary\n",
            "Adding <|az|> to the vocabulary\n",
            "Adding <|sl|> to the vocabulary\n",
            "Adding <|kn|> to the vocabulary\n",
            "Adding <|et|> to the vocabulary\n",
            "Adding <|mk|> to the vocabulary\n",
            "Adding <|br|> to the vocabulary\n",
            "Adding <|eu|> to the vocabulary\n",
            "Adding <|is|> to the vocabulary\n",
            "Adding <|hy|> to the vocabulary\n",
            "Adding <|ne|> to the vocabulary\n",
            "Adding <|mn|> to the vocabulary\n",
            "Adding <|bs|> to the vocabulary\n",
            "Adding <|kk|> to the vocabulary\n",
            "Adding <|sq|> to the vocabulary\n",
            "Adding <|sw|> to the vocabulary\n",
            "Adding <|gl|> to the vocabulary\n",
            "Adding <|mr|> to the vocabulary\n",
            "Adding <|pa|> to the vocabulary\n",
            "Adding <|si|> to the vocabulary\n",
            "Adding <|km|> to the vocabulary\n",
            "Adding <|sn|> to the vocabulary\n",
            "Adding <|yo|> to the vocabulary\n",
            "Adding <|so|> to the vocabulary\n",
            "Adding <|af|> to the vocabulary\n",
            "Adding <|oc|> to the vocabulary\n",
            "Adding <|ka|> to the vocabulary\n",
            "Adding <|be|> to the vocabulary\n",
            "Adding <|tg|> to the vocabulary\n",
            "Adding <|sd|> to the vocabulary\n",
            "Adding <|gu|> to the vocabulary\n",
            "Adding <|am|> to the vocabulary\n",
            "Adding <|yi|> to the vocabulary\n",
            "Adding <|lo|> to the vocabulary\n",
            "Adding <|uz|> to the vocabulary\n",
            "Adding <|fo|> to the vocabulary\n",
            "Adding <|ht|> to the vocabulary\n",
            "Adding <|ps|> to the vocabulary\n",
            "Adding <|tk|> to the vocabulary\n",
            "Adding <|nn|> to the vocabulary\n",
            "Adding <|mt|> to the vocabulary\n",
            "Adding <|sa|> to the vocabulary\n",
            "Adding <|lb|> to the vocabulary\n",
            "Adding <|my|> to the vocabulary\n",
            "Adding <|bo|> to the vocabulary\n",
            "Adding <|tl|> to the vocabulary\n",
            "Adding <|mg|> to the vocabulary\n",
            "Adding <|as|> to the vocabulary\n",
            "Adding <|tt|> to the vocabulary\n",
            "Adding <|haw|> to the vocabulary\n",
            "Adding <|ln|> to the vocabulary\n",
            "Adding <|ha|> to the vocabulary\n",
            "Adding <|ba|> to the vocabulary\n",
            "Adding <|jw|> to the vocabulary\n",
            "Adding <|su|> to the vocabulary\n",
            "Adding <|translate|> to the vocabulary\n",
            "Adding <|transcribe|> to the vocabulary\n",
            "Adding <|startoflm|> to the vocabulary\n",
            "Adding <|startofprev|> to the vocabulary\n",
            "Adding <|nocaptions|> to the vocabulary\n",
            "Adding <|notimestamps|> to the vocabulary\n",
            "The model 'PeftModel' is not supported for . Supported models are ['SpeechEncoderDecoderModel', 'Speech2TextForConditionalGeneration', 'SpeechT5ForSpeechToText', 'WhisperForConditionalGeneration', 'Data2VecAudioForCTC', 'HubertForCTC', 'MCTCTForCTC', 'SEWForCTC', 'SEWDForCTC', 'UniSpeechForCTC', 'UniSpeechSatForCTC', 'Wav2Vec2ForCTC', 'Wav2Vec2ConformerForCTC', 'WavLMForCTC'].\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Running on public URL: https://ee6fef1c-b214-4067.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades (NEW!), check out Spaces: https://huggingface.co/spaces\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div><iframe src=\"https://ee6fef1c-b214-4067.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
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          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import torch\n",
        "import gradio as gr\n",
        "from transformers import (\n",
        "    AutomaticSpeechRecognitionPipeline,\n",
        "    WhisperForConditionalGeneration,\n",
        "    WhisperTokenizer,\n",
        "    WhisperProcessor,\n",
        ")\n",
        "from peft import PeftModel, PeftConfig\n",
        "\n",
        "\n",
        "peft_model_id = \"smangrul/openai-whisper-large-v2-LORA-colab\"\n",
        "language = \"Marathi\"\n",
        "task = \"transcribe\"\n",
        "peft_config = PeftConfig.from_pretrained(peft_model_id)\n",
        "model = WhisperForConditionalGeneration.from_pretrained(\n",
        "    peft_config.base_model_name_or_path, load_in_8bit=True, device_map=\"auto\"\n",
        ")\n",
        "\n",
        "model = PeftModel.from_pretrained(model, peft_model_id)\n",
        "tokenizer = WhisperTokenizer.from_pretrained(peft_config.base_model_name_or_path, language=language, task=task)\n",
        "processor = WhisperProcessor.from_pretrained(peft_config.base_model_name_or_path, language=language, task=task)\n",
        "feature_extractor = processor.feature_extractor\n",
        "forced_decoder_ids = processor.get_decoder_prompt_ids(language=language, task=task)\n",
        "pipe = AutomaticSpeechRecognitionPipeline(model=model, tokenizer=tokenizer, feature_extractor=feature_extractor)\n",
        "\n",
        "\n",
        "def transcribe(audio):\n",
        "    with torch.cuda.amp.autocast():\n",
        "        text = pipe(audio, generate_kwargs={\"forced_decoder_ids\": forced_decoder_ids}, max_new_tokens=255)[\"text\"]\n",
        "    return text\n",
        "\n",
        "\n",
        "iface = gr.Interface(\n",
        "    fn=transcribe,\n",
        "    inputs=gr.Audio(source=\"microphone\", type=\"filepath\"),\n",
        "    outputs=\"text\",\n",
        "    title=\"PEFT LoRA + INT8 Whisper Large V2 Marathi\",\n",
        "    description=\"Realtime demo for Marathi speech recognition using `PEFT-LoRA+INT8` fine-tuned Whisper Large V2 model.\",\n",
        ")\n",
        "\n",
        "iface.launch(share=True)"
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