feature_pipeline.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "75b58048-7d14-4fc6-8085-1fc08c81b4a6",
      "metadata": {
        "id": "75b58048-7d14-4fc6-8085-1fc08c81b4a6"
      },
      "source": [
        "# Fine-Tune Whisper For Multilingual ASR with 🤗 Transformers"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fbfa8ad5-4cdc-4512-9058-836cbbf65e1a",
      "metadata": {
        "id": "fbfa8ad5-4cdc-4512-9058-836cbbf65e1a"
      },
      "source": [
        "In this Colab, we present a step-by-step guide on how to fine-tune Whisper\n",
        "for any multilingual ASR dataset using Hugging Face 🤗 Transformers. This is a\n",
        "more \"hands-on\" version of the accompanying [blog post](https://huggingface.co/blog/fine-tune-whisper).\n",
        "For a more in-depth explanation of Whisper, the Common Voice dataset and the theory behind fine-tuning, the reader is advised to refer to the blog post."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "afe0d503-ae4e-4aa7-9af4-dbcba52db41e",
      "metadata": {
        "id": "afe0d503-ae4e-4aa7-9af4-dbcba52db41e"
      },
      "source": [
        "## Introduction"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9ae91ed4-9c3e-4ade-938e-f4c2dcfbfdc0",
      "metadata": {
        "id": "9ae91ed4-9c3e-4ade-938e-f4c2dcfbfdc0"
      },
      "source": [
        "Whisper is a pre-trained model for automatic speech recognition (ASR)\n",
        "published in [September 2022](https://openai.com/blog/whisper/) by the authors\n",
        "Alec Radford et al. from OpenAI. Unlike many of its predecessors, such as\n",
        "[Wav2Vec 2.0](https://arxiv.org/abs/2006.11477), which are pre-trained\n",
        "on un-labelled audio data, Whisper is pre-trained on a vast quantity of\n",
        "**labelled** audio-transcription data, 680,000 hours to be precise.\n",
        "This is an order of magnitude more data than the un-labelled audio data used\n",
        "to train Wav2Vec 2.0 (60,000 hours). What is more, 117,000 hours of this\n",
        "pre-training data is multilingual ASR data. This results in checkpoints\n",
        "that can be applied to over 96 languages, many of which are considered\n",
        "_low-resource_.\n",
        "\n",
        "When scaled to 680,000 hours of labelled pre-training data, Whisper models\n",
        "demonstrate a strong ability to generalise to many datasets and domains.\n",
        "The pre-trained checkpoints achieve competitive results to state-of-the-art\n",
        "ASR systems, with near 3% word error rate (WER) on the test-clean subset of\n",
        "LibriSpeech ASR and a new state-of-the-art on TED-LIUM with 4.7% WER (_c.f._\n",
        "Table 8 of the [Whisper paper](https://cdn.openai.com/papers/whisper.pdf)).\n",
        "The extensive multilingual ASR knowledge acquired by Whisper during pre-training\n",
        "can be leveraged for other low-resource languages; through fine-tuning, the\n",
        "pre-trained checkpoints can be adapted for specific datasets and languages\n",
        "to further improve upon these results. We'll show just how Whisper can be fine-tuned\n",
        "for low-resource languages in this Colab."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e59b91d6-be24-4b5e-bb38-4977ea143a72",
      "metadata": {
        "id": "e59b91d6-be24-4b5e-bb38-4977ea143a72"
      },
      "source": [
        "<figure>\n",
        "<img src=\"https://raw.githubusercontent.com/sanchit-gandhi/notebooks/main/whisper_architecture.svg\" alt=\"Trulli\" style=\"width:100%\">\n",
        "<figcaption align = \"center\"><b>Figure 1:</b> Whisper model. The architecture\n",
        "follows the standard Transformer-based encoder-decoder model. A\n",
        "log-Mel spectrogram is input to the encoder. The last encoder\n",
        "hidden states are input to the decoder via cross-attention mechanisms. The\n",
        "decoder autoregressively predicts text tokens, jointly conditional on the\n",
        "encoder hidden states and previously predicted tokens. Figure source:\n",
        "<a href=\"https://openai.com/blog/whisper/\">OpenAI Whisper Blog</a>.</figcaption>\n",
        "</figure>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "21b6316e-8a55-4549-a154-66d3da2ab74a",
      "metadata": {
        "id": "21b6316e-8a55-4549-a154-66d3da2ab74a"
      },
      "source": [
        "The Whisper checkpoints come in five configurations of varying model sizes.\n",
        "The smallest four are trained on either English-only or multilingual data.\n",
        "The largest checkpoint is multilingual only. All nine of the pre-trained checkpoints\n",
        "are available on the [Hugging Face Hub](https://huggingface.co/models?search=openai/whisper). The\n",
        "checkpoints are summarised in the following table with links to the models on the Hub:\n",
        "\n",
        "| Size   | Layers | Width | Heads | Parameters | English-only                                         | Multilingual                                      |\n",
        "|--------|--------|-------|-------|------------|------------------------------------------------------|---------------------------------------------------|\n",
        "| tiny   | 4      | 384   | 6     | 39 M       | [✓](https://huggingface.co/openai/whisper-tiny.en)   | [✓](https://huggingface.co/openai/whisper-tiny.)  |\n",
        "| base   | 6      | 512   | 8     | 74 M       | [✓](https://huggingface.co/openai/whisper-base.en)   | [✓](https://huggingface.co/openai/whisper-base)   |\n",
        "| small  | 12     | 768   | 12    | 244 M      | [✓](https://huggingface.co/openai/whisper-small.en)  | [✓](https://huggingface.co/openai/whisper-small)  |\n",
        "| medium | 24     | 1024  | 16    | 769 M      | [✓](https://huggingface.co/openai/whisper-medium.en) | [✓](https://huggingface.co/openai/whisper-medium) |\n",
        "| large  | 32     | 1280  | 20    | 1550 M     | x                                                    | [✓](https://huggingface.co/openai/whisper-large)  |\n",
        "\n",
        "For demonstration purposes, we'll fine-tune the multilingual version of the\n",
        "[`\"small\"`](https://huggingface.co/openai/whisper-small) checkpoint with 244M params (~= 1GB).\n",
        "As for our data, we'll train and evaluate our system on a low-resource language\n",
        "taken from the [Common Voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0)\n",
        "dataset. We'll show that with as little as 8 hours of fine-tuning data, we can achieve\n",
        "strong performance in this language."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3a680dfc-cbba-4f6c-8a1f-e1a5ff3f123a",
      "metadata": {
        "id": "3a680dfc-cbba-4f6c-8a1f-e1a5ff3f123a"
      },
      "source": [
        "------------------------------------------------------------------------\n",
        "\n",
        "\\\\({}^1\\\\) The name Whisper follows from the acronym “WSPSR”, which stands for “Web-scale Supervised Pre-training for Speech Recognition”."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "55fb8d21-df06-472a-99dd-b59567be6dad",
      "metadata": {
        "id": "55fb8d21-df06-472a-99dd-b59567be6dad"
      },
      "source": [
        "## Prepare Environment"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "844a4861-929c-4762-b29b-80b1e95aba4b",
      "metadata": {
        "id": "844a4861-929c-4762-b29b-80b1e95aba4b"
      },
      "source": [
        "First of all, let's try to secure a decent GPU for our Colab! Unfortunately, it's becoming much harder to get access to a good GPU with the free version of Google Colab. However, with Google Colab Pro one should have no issues in being allocated a V100 or P100 GPU.\n",
        "\n",
        "To get a GPU, click _Runtime_ -> _Change runtime type_, then change _Hardware accelerator_ from _None_ to _GPU_."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9abea5d7-9d54-434b-a6bd-399d1b3c6c1a",
      "metadata": {
        "id": "9abea5d7-9d54-434b-a6bd-399d1b3c6c1a"
      },
      "source": [
        "We can verify that we've been assigned a GPU and view its specifications:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "95048026-a3b7-43f0-a274-1bad65e407b4",
      "metadata": {
        "id": "95048026-a3b7-43f0-a274-1bad65e407b4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f1489440-c492-4719-ff27-a42c83d6effa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sat Dec  2 13:50:17 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   55C    P8    10W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "gpu_info = !nvidia-smi\n",
        "gpu_info = '\\n'.join(gpu_info)\n",
        "if gpu_info.find('failed') >= 0:\n",
        "  print('Not connected to a GPU')\n",
        "else:\n",
        "  print(gpu_info)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1d85d613-1c7e-46ac-9134-660bbe7ebc9d",
      "metadata": {
        "id": "1d85d613-1c7e-46ac-9134-660bbe7ebc9d"
      },
      "source": [
        "We'll employ several popular Python packages to fine-tune the Whisper model.\n",
        "We'll use `datasets` to download and prepare our training data and\n",
        "`transformers` to load and train our Whisper model. We'll also require\n",
        "the `soundfile` package to pre-process audio files, `evaluate` and `jiwer` to\n",
        "assess the performance of our model. Finally, we'll\n",
        "use `gradio` to build a flashy demo of our fine-tuned model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e68ea9f8-9b61-414e-8885-3033b67c2850",
      "metadata": {
        "id": "e68ea9f8-9b61-414e-8885-3033b67c2850",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "76b38503-d0ba-45b2-ff73-5dd9ae43341a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25hCollecting typing-extensions~=4.0 (from gradio)\n",
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            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich<14.0.0,>=10.11.0->typer[all]<1.0,>=0.9->gradio) (2.16.1)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich<14.0.0,>=10.11.0->typer[all]<1.0,>=0.9->gradio) (0.1.2)\n",
            "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=8bc8af3f99274a8df88427b7a91535c94ce61a1f5a0cba1cd180e890b7f0502a\n",
            "  Stored in directory: /root/.cache/pip/wheels/01/a6/d1/1c0828c304a4283b2c1639a09ad86f83d7c487ef34c6b4a1bf\n",
            "Successfully built ffmpy\n",
            "Installing collected packages: pydub, ffmpy, websockets, typing-extensions, tomlkit, shellingham, semantic-version, python-multipart, orjson, h11, colorama, annotated-types, aiofiles, uvicorn, starlette, pydantic-core, httpcore, pydantic, httpx, gradio-client, fastapi, gradio\n",
            "  Attempting uninstall: typing-extensions\n",
            "    Found existing installation: typing_extensions 4.5.0\n",
            "    Uninstalling typing_extensions-4.5.0:\n",
            "      Successfully uninstalled typing_extensions-4.5.0\n",
            "  Attempting uninstall: pydantic\n",
            "    Found existing installation: pydantic 1.10.13\n",
            "    Uninstalling pydantic-1.10.13:\n",
            "      Successfully uninstalled pydantic-1.10.13\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.\n",
            "llmx 0.0.15a0 requires cohere, which is not installed.\n",
            "llmx 0.0.15a0 requires openai, which is not installed.\n",
            "llmx 0.0.15a0 requires tiktoken, which is not installed.\n",
            "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.8.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed aiofiles-23.2.1 annotated-types-0.6.0 colorama-0.4.6 fastapi-0.104.1 ffmpy-0.3.1 gradio-4.8.0 gradio-client-0.7.1 h11-0.14.0 httpcore-1.0.2 httpx-0.25.2 orjson-3.9.10 pydantic-2.5.2 pydantic-core-2.14.5 pydub-0.25.1 python-multipart-0.0.6 semantic-version-2.10.0 shellingham-1.5.4 starlette-0.27.0 tomlkit-0.12.0 typing-extensions-4.8.0 uvicorn-0.24.0.post1 websockets-11.0.3\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"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1f60d173-8de1-4ed7-bc9a-d281cf237203",
      "metadata": {
        "id": "1f60d173-8de1-4ed7-bc9a-d281cf237203"
      },
      "source": [
        "We strongly advise you to upload model checkpoints directly the [Hugging Face Hub](https://huggingface.co/)\n",
        "whilst training. The Hub provides:\n",
        "- Integrated version control: you can be sure that no model checkpoint is lost during training.\n",
        "- Tensorboard logs: track important metrics over the course of training.\n",
        "- Model cards: document what a model does and its intended use cases.\n",
        "- Community: an easy way to share and collaborate with the community!\n",
        "\n",
        "Linking the notebook to the Hub is straightforward - it simply requires entering your\n",
        "Hub authentication token when prompted. Find your Hub authentication token [here](https://huggingface.co/settings/tokens):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b045a39e-2a3e-4153-bdb5-281500bcd348",
      "metadata": {
        "id": "b045a39e-2a3e-4153-bdb5-281500bcd348",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "e2e6798c7acd4fe8aaf1f951188a57d3",
            "f84e3236ffb54c9a8b3e9dc917ef2333",
            "a8324e338cfb40d4b0e0f1df95e9d510",
            "b9b3f693993c47799fcf5cd7e39b2722",
            "47045e811b0c4d459c6e0a1e3d077b90",
            "72c6ee752e3f48b2afbd9ec29e1b2509",
            "d3ab7ca6bfb448d2b4b0a6100b0e16a9",
            "fbb90125099f4042b1780f280f4f67f1",
            "b6bb1c0559994a2b8a3aa2be21287f8f",
            "7265feab1e63414c95dcca6a7a36ef86",
            "36dcb319aa9042ea9c58e4b11103b051",
            "7613776bfb5f44548ecc27dd6873d878",
            "eed21a44a7bc48fbadc892358888d26e",
            "3eed23fd056f48abb4695e7177a02c49",
            "17bb281099344fe1aa19099317b7351b",
            "367ae1e81eee42fbb7f160ec4abd1102",
            "7cc000153af1489ea59f2b8a83632318",
            "b6844409566c4a6c814fcb569ea9067f",
            "7ddb7027f73e40efae307c0d0d499a44",
            "e304dfd4cdb54f4c86b629e27b93ffd0",
            "6cc87dec41b14ac5a3333f5ac1f46759",
            "5ec30917a8954c549610faf2ca1bdaee",
            "15d2108146df4c26a1c423375adfd78c",
            "516cb939265b487494c54aee2b4f700d",
            "142e7ad1661f42b49b03f7d4bdf43b96",
            "6370b5fac2cd4f87853f3326dfc0a3d3",
            "74f14c7770c64965a5295161b87eeb99",
            "81e9eafaf552444faaa0ac65b41a5661",
            "9b35e3c59bd0497389094b59d9b44224",
            "95b93dab53f54a28af5e1ab18286d87f",
            "3818385b211249de8cb01dfe6fbbacf9",
            "e12ab3c5cb8d428da0f1ad51b423b989"
          ]
        },
        "outputId": "d4960d5c-9eb8-42dc-8f3c-bc5810269a92"
      },
      "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": "e2e6798c7acd4fe8aaf1f951188a57d3"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b219c9dd-39b6-4a95-b2a1-3f547a1e7bc0",
      "metadata": {
        "id": "b219c9dd-39b6-4a95-b2a1-3f547a1e7bc0"
      },
      "source": [
        "## Load Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "674429c5-0ab4-4adf-975b-621bb69eca38",
      "metadata": {
        "id": "674429c5-0ab4-4adf-975b-621bb69eca38"
      },
      "source": [
        "Using 🤗 Datasets, downloading and preparing data is extremely simple.\n",
        "We can download and prepare the Common Voice splits in just one line of code.\n",
        "\n",
        "First, ensure you have accepted the terms of use on the Hugging Face Hub: [mozilla-foundation/common_voice_11_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0). Once you have accepted the terms, you will have full access to the dataset and be able to download the data locally.\n",
        "\n",
        "Since Italian is both computationally expensive and requires consistent storage resources, we have decided to employ a reduced dataset version not to incur in prohibitive limitations, while still ensuring a good performance."
      ]
    },
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        "outputId": "2cab71d8-a555-4634-f0b5-08836dcefdc0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/datasets/load.py:2088: 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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/8.13k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "57c4ac786d224fc6900b501c7662bf26"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/14.4k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "767bf27b464244c6ac3b03c6e9e64388"
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          "metadata": {}
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        {
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          "name": "stderr",
          "text": [
            "\n",
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            "/usr/local/lib/python3.10/dist-packages/datasets/load.py:2088: 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"
          ]
        },
        {
          "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: 167606\n",
            "    })\n",
            "    test: Dataset({\n",
            "        features: ['client_id', 'path', 'audio', 'sentence', 'up_votes', 'down_votes', 'age', 'gender', 'accent', 'locale', 'segment'],\n",
            "        num_rows: 15003\n",
            "    })\n",
            "})\n"
          ]
        }
      ],
      "source": [
        "from datasets import load_dataset, DatasetDict\n",
        "\n",
        "common_voice = DatasetDict()\n",
        "\n",
        "common_voice[\"train\"] = load_dataset(\"mozilla-foundation/common_voice_11_0\", \"it\", split=\"train+validation\", use_auth_token=True)\n",
        "common_voice[\"test\"] = load_dataset(\"mozilla-foundation/common_voice_11_0\", \"it\", split=\"test\", use_auth_token=True)\n",
        "\n",
        "print(common_voice)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d5c7c3d6-7197-41e7-a088-49b753c1681f",
      "metadata": {
        "id": "d5c7c3d6-7197-41e7-a088-49b753c1681f"
      },
      "source": [
        "Most ASR datasets only provide input audio samples (`audio`) and the\n",
        "corresponding transcribed text (`sentence`). Common Voice contains additional\n",
        "metadata information, such as `accent` and `locale`, which we can disregard for ASR.\n",
        "Keeping the notebook as general as possible, we only consider the input audio and\n",
        "transcribed text for fine-tuning, discarding the additional metadata information:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "20ba635d-518c-47ac-97ee-3cad25f1e0ce",
      "metadata": {
        "id": "20ba635d-518c-47ac-97ee-3cad25f1e0ce",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4f66287d-7d34-43e8-c475-977d2334332f"
      },
      "outputs": [
        {
          "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: 15000\n",
            "    })\n",
            "    test: Dataset({\n",
            "        features: ['client_id', 'path', 'audio', 'sentence', 'up_votes', 'down_votes', 'age', 'gender', 'accent', 'locale', 'segment'],\n",
            "        num_rows: 5000\n",
            "    })\n",
            "})\n"
          ]
        }
      ],
      "source": [
        "#common_voice = common_voice.remove_columns([\"accent\", \"age\", \"client_id\", \"down_votes\", \"gender\", \"locale\", \"path\", \"segment\", \"up_votes\"])\n",
        "common_voice[\"train\"] = common_voice[\"train\"].shuffle(seed=42)\n",
        "common_voice[\"train\"] = common_voice['train'].select(range(15000))\n",
        "common_voice[\"test\"] = common_voice[\"test\"].shuffle(seed=42)\n",
        "common_voice[\"test\"] = common_voice['test'].select(range(5000))\n",
        "\n",
        "print(common_voice)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2d63b2d2-f68a-4d74-b7f1-5127f6d16605",
      "metadata": {
        "id": "2d63b2d2-f68a-4d74-b7f1-5127f6d16605"
      },
      "source": [
        "## Prepare Feature Extractor, Tokenizer and Data"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "601c3099-1026-439e-93e2-5635b3ba5a73",
      "metadata": {
        "id": "601c3099-1026-439e-93e2-5635b3ba5a73"
      },
      "source": [
        "The ASR pipeline can be de-composed into three stages:\n",
        "1) A feature extractor which pre-processes the raw audio-inputs\n",
        "2) The model which performs the sequence-to-sequence mapping\n",
        "3) A tokenizer which post-processes the model outputs to text format\n",
        "\n",
        "In 🤗 Transformers, the Whisper model has an associated feature extractor and tokenizer,\n",
        "called [WhisperFeatureExtractor](https://huggingface.co/docs/transformers/main/model_doc/whisper#transformers.WhisperFeatureExtractor)\n",
        "and [WhisperTokenizer](https://huggingface.co/docs/transformers/main/model_doc/whisper#transformers.WhisperTokenizer)\n",
        "respectively.\n",
        "\n",
        "We'll go through details for setting-up the feature extractor and tokenizer one-by-one!"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "560332eb-3558-41a1-b500-e83a9f695f84",
      "metadata": {
        "id": "560332eb-3558-41a1-b500-e83a9f695f84"
      },
      "source": [
        "### Load WhisperFeatureExtractor"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "32ec8068-0bd7-412d-b662-0edb9d1e7365",
      "metadata": {
        "id": "32ec8068-0bd7-412d-b662-0edb9d1e7365"
      },
      "source": [
        "The Whisper feature extractor performs two operations:\n",
        "1. Pads / truncates the audio inputs to 30s: any audio inputs shorter than 30s are padded to 30s with silence (zeros), and those longer that 30s are truncated to 30s\n",
        "2. Converts the audio inputs to _log-Mel spectrogram_ input features, a visual representation of the audio and the form of the input expected by the Whisper model"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "589d9ec1-d12b-4b64-93f7-04c63997da19",
      "metadata": {
        "id": "589d9ec1-d12b-4b64-93f7-04c63997da19"
      },
      "source": [
        "<figure>\n",
        "<img src=\"https://raw.githubusercontent.com/sanchit-gandhi/notebooks/main/spectrogram.jpg\" alt=\"Trulli\" style=\"width:100%\">\n",
        "<figcaption align = \"center\"><b>Figure 2:</b> Conversion of sampled audio array to log-Mel spectrogram.\n",
        "Left: sampled 1-dimensional audio signal. Right: corresponding log-Mel spectrogram. Figure source:\n",
        "<a href=\"https://ai.googleblog.com/2019/04/specaugment-new-data-augmentation.html\">Google SpecAugment Blog</a>.\n",
        "</figcaption>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b2ef54d5-b946-4c1d-9fdc-adc5d01b46aa",
      "metadata": {
        "id": "b2ef54d5-b946-4c1d-9fdc-adc5d01b46aa"
      },
      "source": [
        "We'll load the feature extractor from the pre-trained checkpoint with the default values:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bc77d7bb-f9e2-47f5-b663-30f7a4321ce5",
      "metadata": {
        "id": "bc77d7bb-f9e2-47f5-b663-30f7a4321ce5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "6e151a25b3c34625a4d5f1bd4af9dedb",
            "84a46a2e64fd494d8d00bc0e589e942f",
            "df24a4f57caf4093b230257488ac73a7",
            "18741f707c004a6f86be0f5b961a8bbc",
            "679f30debc5e4eae90f8eedfed7cbb08",
            "fb84c5054c4d44c79417dd6722543808",
            "a9f0a8812fb740b99f74ceedeb561b6d",
            "ce717d8c321544f68931bf1a029178b0",
            "d7192355ab974b258c334ce34afa2de3",
            "684ccf34f9304e2fb1d43d33845e8229",
            "a322ddea8cda497182fd324f1cc42c10"
          ]
        },
        "outputId": "9f6bc9c6-6772-44a1-d263-25c4906da831"
      },
      "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": "6e151a25b3c34625a4d5f1bd4af9dedb"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import WhisperFeatureExtractor\n",
        "\n",
        "feature_extractor = WhisperFeatureExtractor.from_pretrained(\"openai/whisper-small\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "93748af7-b917-4ecf-a0c8-7d89077ff9cb",
      "metadata": {
        "id": "93748af7-b917-4ecf-a0c8-7d89077ff9cb"
      },
      "source": [
        "### Load WhisperTokenizer"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2bc82609-a9fb-447a-a2af-99597c864029",
      "metadata": {
        "id": "2bc82609-a9fb-447a-a2af-99597c864029"
      },
      "source": [
        "The Whisper model outputs a sequence of _token ids_. The tokenizer maps each of these token ids to their corresponding text string. For Hindi, we can load the pre-trained tokenizer and use it for fine-tuning without any further modifications. We simply have to\n",
        "specify the target language and the task. These arguments inform the\n",
        "tokenizer to prefix the language and task tokens to the start of encoded\n",
        "label sequences:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c7b07f9b-ae0e-4f89-98f0-0c50d432eab6",
      "metadata": {
        "id": "c7b07f9b-ae0e-4f89-98f0-0c50d432eab6",
        "colab": {
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          "height": 241,
          "referenced_widgets": [
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        },
        "outputId": "bae10608-cdbb-4216-80dc-de48eda380d7"
      },
      "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": "580347b782e24ec9a5755cfbe65fb8f1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/836k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6feaad42cddb40689b2eddc9f403c1ef"
            }
          },
          "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": "09cf6ad7d9c1414396f786b0632a88a4"
            }
          },
          "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": "bb36ede8106c4819a9a535fee5dccbe0"
            }
          },
          "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": "3c31de1a7eff4d01ac559a97251f01ba"
            }
          },
          "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": "4de85cab234243c0ba7cabc45f5f2171"
            }
          },
          "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": "ce0ee7820c5c4d58b011573f23d9b79c"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import WhisperTokenizer\n",
        "\n",
        "tokenizer = WhisperTokenizer.from_pretrained(\"openai/whisper-small\", language=\"Italian\", task=\"transcribe\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d2ef23f3-f4a8-483a-a2dc-080a7496cb1b",
      "metadata": {
        "id": "d2ef23f3-f4a8-483a-a2dc-080a7496cb1b"
      },
      "source": [
        "### Combine To Create A WhisperProcessor"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5ff67654-5a29-4bb8-a69d-0228946c6f8d",
      "metadata": {
        "id": "5ff67654-5a29-4bb8-a69d-0228946c6f8d"
      },
      "source": [
        "To simplify using the feature extractor and tokenizer, we can _wrap_\n",
        "both into a single `WhisperProcessor` class. This processor object\n",
        "inherits from the `WhisperFeatureExtractor` and `WhisperProcessor`,\n",
        "and can be used on the audio inputs and model predictions as required.\n",
        "In doing so, we only need to keep track of two objects during training:\n",
        "the `processor` and the `model`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "77d9f0c5-8607-4642-a8ac-c3ab2e223ea6",
      "metadata": {
        "id": "77d9f0c5-8607-4642-a8ac-c3ab2e223ea6"
      },
      "outputs": [],
      "source": [
        "from transformers import WhisperProcessor\n",
        "\n",
        "processor = WhisperProcessor.from_pretrained(\"openai/whisper-small\", language=\"Italian\", task=\"transcribe\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "381acd09-0b0f-4d04-9eb3-f028ac0e5f2c",
      "metadata": {
        "id": "381acd09-0b0f-4d04-9eb3-f028ac0e5f2c"
      },
      "source": [
        "### Prepare Data"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9649bf01-2e8a-45e5-8fca-441c13637b8f",
      "metadata": {
        "id": "9649bf01-2e8a-45e5-8fca-441c13637b8f"
      },
      "source": [
        "Let's print the first example of the Common Voice dataset to see\n",
        "what form the data is in:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6e6b0ec5-0c94-4e2c-ae24-c791be1b2255",
      "metadata": {
        "id": "6e6b0ec5-0c94-4e2c-ae24-c791be1b2255",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6b17cb4a-3cec-4914-c436-9d2e62b52d4a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'client_id': '1b671d64d6d1cffa126ef1cf1e447abd13b34cf08880f5480bf23894174a7732b550cfef0b526e9519460f4576f51109d0567cfe824ac1ba78ed09b4b0250725', 'path': '/root/.cache/huggingface/datasets/downloads/extracted/60106da21e955b6e07e96f50324737e94220b976ca6f0ecd2ac109997d817997/it_train_0/common_voice_it_25949981.mp3', 'audio': {'path': '/root/.cache/huggingface/datasets/downloads/extracted/60106da21e955b6e07e96f50324737e94220b976ca6f0ecd2ac109997d817997/it_train_0/common_voice_it_25949981.mp3', 'array': array([ 2.27373675e-13, -3.86535248e-12, -6.30961949e-12, ...,\n",
            "       -5.13360199e-09, -1.08973630e-09,  1.18904975e-09]), 'sampling_rate': 48000}, 'sentence': 'Variazioni locali del pH possono avere lo stesso effetto.', 'up_votes': 3, 'down_votes': 0, 'age': '', 'gender': 'male', 'accent': '', 'locale': 'it', 'segment': ''}\n"
          ]
        }
      ],
      "source": [
        "print(common_voice[\"train\"][0])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5a679f05-063d-41b3-9b58-4fc9c6ccf4fd",
      "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:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f12e2e57-156f-417b-8cfb-69221cc198e8",
      "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))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "00382a3e-abec-4cdd-a54c-d1aaa3ea4707",
      "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:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "87122d71-289a-466a-afcf-fa354b18946b",
      "metadata": {
        "id": "87122d71-289a-466a-afcf-fa354b18946b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7775dcc3-2cc6-4a83-ca96-b98a5eba01ff"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'client_id': '1b671d64d6d1cffa126ef1cf1e447abd13b34cf08880f5480bf23894174a7732b550cfef0b526e9519460f4576f51109d0567cfe824ac1ba78ed09b4b0250725', 'path': '/root/.cache/huggingface/datasets/downloads/extracted/60106da21e955b6e07e96f50324737e94220b976ca6f0ecd2ac109997d817997/it_train_0/common_voice_it_25949981.mp3', 'audio': {'path': '/root/.cache/huggingface/datasets/downloads/extracted/60106da21e955b6e07e96f50324737e94220b976ca6f0ecd2ac109997d817997/it_train_0/common_voice_it_25949981.mp3', 'array': array([ 6.98491931e-10,  1.62981451e-09,  1.04773790e-09, ...,\n",
            "       -1.17853984e-08, -7.82030529e-09, -1.79254855e-09]), 'sampling_rate': 16000}, 'sentence': 'Variazioni locali del pH possono avere lo stesso effetto.', 'up_votes': 3, 'down_votes': 0, 'age': '', 'gender': 'male', 'accent': '', 'locale': 'it', 'segment': ''}\n"
          ]
        }
      ],
      "source": [
        "print(common_voice[\"train\"][0])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "91edc72d-08f8-4f01-899d-74e65ce441fc",
      "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."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6525c478-8962-4394-a1c4-103c54cce170",
      "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"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "70b319fb-2439-4ef6-a70d-a47bf41c4a13",
      "metadata": {
        "id": "70b319fb-2439-4ef6-a70d-a47bf41c4a13"
      },
      "source": [
        "We can apply the data preparation function to all of our training examples using 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."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7b73ab39-ffaf-4b9e-86e5-782963c6134b",
      "metadata": {
        "id": "7b73ab39-ffaf-4b9e-86e5-782963c6134b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "934dcb7bb3164aa79c50e0537a667e23",
            "ab496744b8f7464f9559085916b7ccbd",
            "968bb0c6dbdb42f9a25fc4fef5822aee",
            "c8596123e9574d2a9cb5071ac29a6c96",
            "1538f2f8e4ef44c28ba44a1655d502a1",
            "fb9398f90c1d4ffc8d606c5303102366",
            "ae052ec998fc4ec3940ece062d827f36",
            "742b748a15de4d73b8081f8013c2aea6",
            "942a4f340a264946bff338902016fd2d",
            "0ed2c84060e44ccda3764cb340978751",
            "5c8d680d2c994135bc8d9c6ca05a1bc8",
            "0e969e4673b7470bbe75ac98b6886814",
            "1fe3d359688c46e499a192c4d93530c9",
            "e3107d1646bc448f949c9ad76397e6e5",
            "698cd19464d74fe38eacba0e2daccdd9",
            "bf111227a5114bc5be2f77d3ad97d1dd",
            "1f653f17072b448184c03b306232d3c6",
            "c2cbefcb30d2430c8b735e7cde93da94",
            "1d40aff6b5cc45b4888ee4d845104267",
            "3cbc95a8778b443286ca7874a4704b7a",
            "8174df419a4d45bca2dea5fc19ccada0",
            "2e270443e15a4111b19ce3572df2c96e"
          ]
        },
        "outputId": "1383f71f-c749-4cca-dcb5-340cf1c6f21c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map (num_proc=3):   0%|          | 0/15000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "934dcb7bb3164aa79c50e0537a667e23"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map (num_proc=3):   0%|          | 0/5000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0e969e4673b7470bbe75ac98b6886814"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice.column_names[\"train\"], num_proc=3)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To save the processed data and be able to access it in the training pipeline, we have employed Google Drive's storage, mounting the drive for easy access."
      ],
      "metadata": {
        "id": "wUq130oUDTaH"
      },
      "id": "wUq130oUDTaH"
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('dataset_sv', force_remount = True)"
      ],
      "metadata": {
        "id": "d7JWZIXVMarv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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          "text": [
            "Mounted at dataset_sv\n"
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        }
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        "import os\n",
        "\n",
        "os.mkdir('dataset_sv/MyDrive/dataset_sv/')\n",
        "os.mkdir('dataset_sv/MyDrive/dataset_sv/train')\n",
        "os.mkdir('dataset_sv/MyDrive/dataset_sv/test')\n",
        "\n",
        "def get_size(start_path = '.'):\n",
        "    total_size = 0\n",
        "    for dirpath, dirnames, filenames in os.walk(start_path):\n",
        "        for f in filenames:\n",
        "            fp = os.path.join(dirpath, f)\n",
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              "Saving the dataset (0/29 shards):   0%|          | 0/15000 [00:00<?, ? examples/s]"
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