WhisperTiny_Finetune_Hindi.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Aryan-401/GoogleColabProjects/blob/main/WhisperTiny_Finetune_Hindi.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "https://github.com/libindic/indic-trans\n",
        "\"\"\"\n",
        "!git clone https://github.com/libindic/indic-trans.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RIgXvhxJCou5",
        "outputId": "02bb0a25-97a9-41bc-9d65-0f79ead922da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'indic-trans'...\n",
            "remote: Enumerating objects: 2225, done.\u001b[K\n",
            "remote: Counting objects: 100% (19/19), done.\u001b[K\n",
            "remote: Compressing objects: 100% (16/16), done.\u001b[K\n",
            "remote: Total 2225 (delta 8), reused 10 (delta 3), pack-reused 2206\u001b[K\n",
            "Receiving objects: 100% (2225/2225), 516.51 MiB | 22.89 MiB/s, done.\n",
            "Resolving deltas: 100% (1102/1102), done.\n",
            "Updating files: 100% (719/719), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -r indic-trans/requirements.txt"
      ],
      "metadata": {
        "id": "qIBCRwqeCwbT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e1358068-ed9c-4c08-ffec-cca9268608f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pbr (from -r indic-trans/requirements.txt (line 1))\n",
            "  Downloading pbr-6.0.0-py2.py3-none-any.whl (107 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/107.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m107.5/107.5 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from -r indic-trans/requirements.txt (line 2)) (1.16.0)\n",
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            "Requirement already satisfied: numpy>=1.10.4 in /usr/local/lib/python3.10/dist-packages (from -r indic-trans/requirements.txt (line 5)) (1.23.5)\n",
            "Requirement already satisfied: scipy>=0.13.3 in /usr/local/lib/python3.10/dist-packages (from -r indic-trans/requirements.txt (line 6)) (1.11.4)\n",
            "Installing collected packages: pbr\n",
            "Successfully installed pbr-6.0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install indic-trans/."
      ],
      "metadata": {
        "id": "P8BiHJ0fCwYn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5d51e77e-5d3f-461e-d60c-67e2a4712659"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing ./indic-trans\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: pbr in /usr/local/lib/python3.10/dist-packages (from indictrans==1.2.3) (6.0.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from indictrans==1.2.3) (1.16.0)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.10/dist-packages (from indictrans==1.2.3) (0.18.3)\n",
            "Requirement already satisfied: cython>=0.24.0a0 in /usr/local/lib/python3.10/dist-packages (from indictrans==1.2.3) (3.0.6)\n",
            "Requirement already satisfied: numpy>=1.10.4 in /usr/local/lib/python3.10/dist-packages (from indictrans==1.2.3) (1.23.5)\n",
            "Requirement already satisfied: scipy>=0.13.3 in /usr/local/lib/python3.10/dist-packages (from indictrans==1.2.3) (1.11.4)\n",
            "Building wheels for collected packages: indictrans\n",
            "  Building wheel for indictrans (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for indictrans: filename=indictrans-1.2.3-cp310-cp310-linux_x86_64.whl size=337822735 sha256=d1389760c76720f281cf51e1afa1518bd5510cd7b0382f0dfe6d29eb9ba83298\n",
            "  Stored in directory: /root/.cache/pip/wheels/3e/c9/43/39c5aaa9a570043089bac219a37343294fa5b47f1350dea53a\n",
            "Successfully built indictrans\n",
            "Installing collected packages: indictrans\n",
            "Successfully installed indictrans-1.2.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ms_Pr9_-CwQ-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "50b59956-39ea-4699-c630-0fe9df0bc5ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "congress party adhyaksh sonia gandhi, tamilnadu kii mukhyamantri jayalalita or reserve bank ke governor raghuram rajan ke bich ek samaantahai. ye sabhi alag-alag kaarnon se bhartiya janata party ke rajyasabha saansad subramanyam swami ke nishane par hai. unke jayalalita or sonia gandhi ke peeche padane kaa kaaran kathith bhrashtachar he.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eyggt02YSPZN",
        "outputId": "ae4cf77e-9937-478c-b0f3-160ff460371c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Collecting datasets[audio]\n",
            "  Downloading datasets-2.16.0-py3-none-any.whl (507 kB)\n",
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            "\u001b[?25hCollecting evaluate\n",
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            "Requirement already satisfied: platformdirs>=2.5.0 in /usr/local/lib/python3.10/dist-packages (from pooch>=1.0->librosa->datasets[audio]) (4.1.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets[audio]) (1.16.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.20.0->librosa->datasets[audio]) (3.2.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch!=1.12.0,>=1.10->transformers[torch]) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch!=1.12.0,>=1.10->transformers[torch]) (1.3.0)\n",
            "Installing collected packages: rapidfuzz, pyarrow-hotfix, dill, responses, multiprocess, jiwer, accelerate, datasets, evaluate\n",
            "Successfully installed accelerate-0.25.0 datasets-2.16.0 dill-0.3.7 evaluate-0.4.1 jiwer-3.0.3 multiprocess-0.70.15 pyarrow-hotfix-0.6 rapidfuzz-3.6.0 responses-0.18.0\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers[torch] datasets[audio] evaluate jiwer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OgLfUJME-iLB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162,
          "referenced_widgets": [
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            "e67d17f5c27848df95a2b2fdf298e75b",
            "9cdb24515a514b71aaafd7a0efe3a054",
            "d3eaee6f69de4e6ab0ecbddc7fabc3f2",
            "2c298a9f3e174fbc9d633a47fc66ba88",
            "3bf513393aae40e2a4af71a21476379f",
            "7abd4cb10a0e4b4e859cdd3799c62709",
            "c980782af36a44e8a32bcb31da8647fa",
            "452ecc37834a4fffa4ab359b99d76dd9",
            "895dd3e13c194faea2ff06585d1e4144",
            "0e6e587cbb794e56accf8d48775c4022",
            "0e4241a51cca4b92998a18cc66cbf30a",
            "8b495ceee51e49daa732c17d9bb0129a",
            "c337f005327b4028b85c5f12438c299a",
            "c388f36a8379495182e8b97ffbc028e8"
          ]
        },
        "outputId": "f1d627fe-07c0-4c56-b2d2-f9fcc5f7aa57"
      },
      "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": "5b02aa46d36f4d1aa49e4f5ce3f5f1c1"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "530hleUz-iIk"
      },
      "outputs": [],
      "source": [
        "from datasets import load_dataset, DatasetDict\n",
        "\n",
        "dataset = load_dataset(\"google/fleurs\", 'hi_in', split='train+test')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K7fC86_P-iD6",
        "outputId": "d8e41eb7-2cc7-4e8c-f8f1-5ac33b0efac1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset({\n",
            "    features: ['id', 'num_samples', 'path', 'audio', 'transcription', 'raw_transcription', 'gender', 'lang_id', 'language', 'lang_group_id'],\n",
            "    num_rows: 2538\n",
            "})\n"
          ]
        }
      ],
      "source": [
        "print(dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xBUZ0OJY-iBB"
      },
      "outputs": [],
      "source": [
        "dataset = dataset.select_columns([\"audio\", \"transcription\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nONOJdXl-h5j"
      },
      "outputs": [],
      "source": [
        "from transformers import WhisperProcessor\n",
        "\n",
        "processor = WhisperProcessor.from_pretrained(\n",
        "    \"openai/whisper-tiny\", language=\"hindi\", task=\"transcribe\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WinPMr6b_pvu"
      },
      "outputs": [],
      "source": [
        "from datasets import Audio\n",
        "\n",
        "sampling_rate = processor.feature_extractor.sampling_rate\n",
        "dataset = dataset.cast_column(\"audio\", Audio(sampling_rate=sampling_rate))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8KUFjlrdH5aJ",
        "outputId": "59271fd6-60c4-4688-b112-1455020472b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['audio', 'transcription'],\n",
              "    num_rows: 2538\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rc345RSL_xbw"
      },
      "outputs": [],
      "source": [
        "from indictrans import Transliterator\n",
        "\n",
        "def convert_hindi(example):\n",
        "    audio = example[\"audio\"]\n",
        "    trn = Transliterator(source='hin', target='eng', build_lookup=True)\n",
        "    example = processor(\n",
        "        audio=audio[\"array\"],\n",
        "        sampling_rate=audio[\"sampling_rate\"],\n",
        "        text=trn.transform(example['transcription']),\n",
        "    )\n",
        "    example[\"input_length\"] = len(audio[\"array\"]) / audio[\"sampling_rate\"]\n",
        "    return example\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "woLIyUNL_xZc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "373460f6ca67475183240c93b528106a",
            "1a99617e7c0b47a69e44777fa7852ab1",
            "1239f16db90140b995f7bb60c1e96bbd",
            "fdb97da9c2674b4ba811c6c8084ac2b1",
            "b85152aea0244e1d95fee9b4912ac4ae",
            "0c583073eff445dfac4d1bc3069429f3",
            "0b9013810a914a2a8a734d5e1dff74bb",
            "90dd770c0f3641d78d2269763c5b7a36",
            "f10ab1543008439d92a86a4ccdd3784b",
            "287bd6cd6ab84995bc502b9abc4ef17c",
            "2a948d2684fa46e18f17fc7044734e47"
          ]
        },
        "outputId": "90129da2-f51d-4723-f67c-de0abaf5f0d0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2538 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "373460f6ca67475183240c93b528106a"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "dataset = dataset.map(\n",
        "    convert_hindi, remove_columns=dataset.column_names ,num_proc=1, batch_size=100\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CxBhmxzjTpWX",
        "outputId": "8c8bfcb2-c810-493b-b0fd-9682ee5e2d17"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['input_features', 'labels', 'input_length'],\n",
              "    num_rows: 2538\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ],
      "source": [
        "dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Te8dYZST9CIH"
      },
      "outputs": [],
      "source": [
        "dataset = dataset.train_test_split(0.1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GIrpezrjhVuz",
        "outputId": "1cb4f002-d49a-4d15-b7a2-40f80eaf21ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['input_features', 'labels', 'input_length'],\n",
              "        num_rows: 2284\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['input_features', 'labels', 'input_length'],\n",
              "        num_rows: 254\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "from dataclasses import dataclass\n",
        "from typing import Any, Dict, List, Union\n",
        "\n",
        "\n",
        "\n",
        "class DataCollatorSpeechSeq2SeqWithPadding:\n",
        "    processor: Any\n",
        "\n",
        "    def __init__(self, processor):\n",
        "        self.processor = processor\n",
        "\n",
        "    def __call__(\n",
        "        self, features: List[Dict[str, Union[List[int], torch.Tensor]]]\n",
        "    ) -> 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 = [\n",
        "            {\"input_features\": feature[\"input_features\"][0]} for feature in features\n",
        "        ]\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(\n",
        "            labels_batch.attention_mask.ne(1), -100\n",
        "        )\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"
      ],
      "metadata": {
        "id": "ZkHv0n5IRWcA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_collator = DataCollatorSpeechSeq2SeqWithPadding(processor=processor)"
      ],
      "metadata": {
        "id": "y-hNJt59RitK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import evaluate\n",
        "\n",
        "metric = evaluate.load(\"wer\")"
      ],
      "metadata": {
        "id": "8_LAXsxRRkGA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers.models.whisper.english_normalizer import BasicTextNormalizer\n",
        "\n",
        "normalizer = BasicTextNormalizer()\n",
        "\n",
        "\n",
        "def compute_metrics(pred):\n",
        "    pred_ids = pred.predictions\n",
        "    label_ids = pred.label_ids\n",
        "\n",
        "    # replace -100 with the pad_token_id\n",
        "    label_ids[label_ids == -100] = processor.tokenizer.pad_token_id\n",
        "\n",
        "    # we do not want to group tokens when computing the metrics\n",
        "    pred_str = processor.batch_decode(pred_ids, skip_special_tokens=True)\n",
        "    label_str = processor.batch_decode(label_ids, skip_special_tokens=True)\n",
        "\n",
        "    # compute orthographic wer\n",
        "    wer_ortho = metric.compute(predictions=pred_str, references=label_str)\n",
        "\n",
        "    # compute normalised WER\n",
        "    pred_str_norm = [normalizer(pred) for pred in pred_str]\n",
        "    label_str_norm = [normalizer(label) for label in label_str]\n",
        "    # filtering step to only evaluate the samples that correspond to non-zero references:\n",
        "    pred_str_norm = [\n",
        "        pred_str_norm[i] for i in range(len(pred_str_norm)) if len(label_str_norm[i]) > 0\n",
        "    ]\n",
        "    label_str_norm = [\n",
        "        label_str_norm[i]\n",
        "        for i in range(len(label_str_norm))\n",
        "        if len(label_str_norm[i]) > 0\n",
        "    ]\n",
        "\n",
        "    wer = metric.compute(predictions=pred_str_norm, references=label_str_norm)\n",
        "\n",
        "    return {\"wer_ortho\": wer_ortho, \"wer\": wer}"
      ],
      "metadata": {
        "id": "MW4gLKvWRlK3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import WhisperForConditionalGeneration\n",
        "\n",
        "model = WhisperForConditionalGeneration.from_pretrained(\"openai/whisper-tiny\")"
      ],
      "metadata": {
        "id": "mkaCLws-Rmgj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from functools import partial\n",
        "\n",
        "# disable cache during training since it's incompatible with gradient checkpointing\n",
        "model.config.use_cache = False\n",
        "\n",
        "# set language and task for generation and re-enable cache\n",
        "model.generate = partial(\n",
        "    model.generate, language=\"hindi\", task=\"transcribe\", use_cache=True\n",
        ")"
      ],
      "metadata": {
        "id": "XrPwCkGNRoJD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Seq2SeqTrainingArguments\n",
        "\n",
        "from transformers import Seq2SeqTrainingArguments\n",
        "\n",
        "training_args = Seq2SeqTrainingArguments(\n",
        "    output_dir=\"./whisper-tiny-finetune-hindi-fleurs\",  # name on the HF Hub\n",
        "    per_device_train_batch_size=32,\n",
        "    gradient_accumulation_steps=1,  # increase by 2x for every 2x decrease in batch size\n",
        "    learning_rate=1e-5,\n",
        "    lr_scheduler_type=\"constant_with_warmup\",\n",
        "    warmup_steps=50,\n",
        "    max_steps=500,  # increase to 4000 if you have your own GPU or a Colab paid plan\n",
        "    gradient_checkpointing=True,\n",
        "    fp16=True,\n",
        "    fp16_full_eval=True,\n",
        "    evaluation_strategy=\"steps\",\n",
        "    per_device_eval_batch_size=32,\n",
        "    predict_with_generate=True,\n",
        "    generation_max_length=225,\n",
        "    save_steps=100,\n",
        "    eval_steps=100,\n",
        "    logging_steps=25,\n",
        "    report_to=[\"tensorboard\"],\n",
        "    load_best_model_at_end=True,\n",
        "    metric_for_best_model=\"wer\",\n",
        "    greater_is_better=False,\n",
        "    push_to_hub=True,\n",
        ")"
      ],
      "metadata": {
        "id": "MiU6iYWURpkf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Seq2SeqTrainer\n",
        "\n",
        "trainer = Seq2SeqTrainer(\n",
        "    args=training_args,\n",
        "    model=model,\n",
        "    train_dataset=dataset[\"train\"],\n",
        "    eval_dataset=dataset[\"test\"],\n",
        "    data_collator=data_collator,\n",
        "    compute_metrics=compute_metrics,\n",
        "    tokenizer=processor,\n",
        ")"
      ],
      "metadata": {
        "id": "mAflmLO6RrYC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "kwargs = {\n",
        "     \"dataset_tags\": \"google/fleurs\",\n",
        "    \"finetuned_from\": \"openai/whisper-tiny\",\n",
        "    \"tasks\": \"automatic-speech-recognition\",\n",
        "}"
      ],
      "metadata": {
        "id": "A4PZLr72R2WR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "id": "WSH9oHerSHqk",
        "outputId": "67210636-db0e-49cc-e76c-56ba7e3d5dc7"
      },
      "execution_count": null,
      "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='500' max='500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [500/500 52:36, Epoch 6/7]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Wer Ortho</th>\n",
              "      <th>Wer</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>1.811200</td>\n",
              "      <td>1.727381</td>\n",
              "      <td>0.632327</td>\n",
              "      <td>0.625800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>1.038700</td>\n",
              "      <td>1.119369</td>\n",
              "      <td>0.512999</td>\n",
              "      <td>0.507202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>0.767100</td>\n",
              "      <td>0.967135</td>\n",
              "      <td>0.466494</td>\n",
              "      <td>0.461268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>0.528300</td>\n",
              "      <td>0.884007</td>\n",
              "      <td>0.449378</td>\n",
              "      <td>0.443982</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.445800</td>\n",
              "      <td>0.831486</td>\n",
              "      <td>0.431293</td>\n",
              "      <td>0.426216</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "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: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: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: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",
            "There were missing keys in the checkpoint model loaded: ['proj_out.weight'].\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=500, training_loss=1.2182455615997314, metrics={'train_runtime': 3166.8649, 'train_samples_per_second': 5.052, 'train_steps_per_second': 0.158, 'total_flos': 3.909478957056e+17, 'train_loss': 1.2182455615997314, 'epoch': 6.94})"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.push_to_hub(**kwargs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "SLAjs4sjSIwU",
        "outputId": "ee239cc2-418c-4819-97be-99dfffeac4fa"
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