Speech_Recognition_FineTune_Tiny.ipynb

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      "metadata": {
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Aryan-401/GoogleColabProjects/blob/main/Speech_Recognition_FineTune_Tiny.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers[torch] datasets[audio] evaluate jiwer"
      ],
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        },
        "id": "CM1S0VCfxfLS",
        "outputId": "b4c22647-dd17-42f8-9590-1d23d502549c"
      },
      "execution_count": 1,
      "outputs": [
        {
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          "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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            "\u001b[?25hCollecting jiwer\n",
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            "\u001b[?25hRequirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets[audio]) (10.0.1)\n",
            "Collecting pyarrow-hotfix (from datasets[audio])\n",
            "  Downloading pyarrow_hotfix-0.6-py3-none-any.whl (7.9 kB)\n",
            "Collecting dill<0.3.8,>=0.3.0 (from datasets[audio])\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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162,
          "referenced_widgets": [
            "a6c9ae7069dc42b3b590430a2ec4bc92",
            "d5ffa13d79fb43ab96d07e1cbe8f48c8",
            "f28e8a78b66945f1835fc77cd5b516c0",
            "8cb497e86cd145218bfbd73ae59f7f8a",
            "97e54177b32949e7ad2fde1e29ff783d",
            "fe7c4b6baf544876beeb0f75326e98aa",
            "6a583efa775e43dabee3f30a1d21935d",
            "71e2f1acdf394a32bbb7b8b6bca56fed",
            "48e564911c4b413198e7dd23a24fa09c",
            "08c6adf0862f4b839a1cf21dd52c403f",
            "3d7fb64b845045139746a47f11e7e4e8",
            "244ae969a6324daebcca0ebc95af0a20",
            "19ed7fb3211e473fa8eecc75659c61ef",
            "968bf0aa12ce4ecba7e03b1eea50a53e",
            "2c9cd09b704b487cb89b851de8a7c00c",
            "a773e7332d954756837a28fe22aaa77e",
            "a5eb57c4e60f4742abef159ce52fd90e",
            "cc0f3d626a1c4a758733a95ef00b54ed",
            "d1af7975faf44c8b8a49409ff889ed83",
            "f76241bdad5e4c66ae23bdaee406bd12",
            "bbe009a0d0cb44a0a79174834adf9e16",
            "9d25ce4e1c1044daa048ea55074949bf",
            "a992ef6163d94e42b5f0600b40ce8119",
            "45a90ee639c147d4b6acdb0ba485c40f",
            "b22ea7a50ba641fc98b64fdadba65e90",
            "806bb1875b31465ea33bbb97804a5b45",
            "35af2946551e4bc7ae8dce212f0a9eb4",
            "811bdb21ac4b4a4b868f331e92bc656e",
            "2b929beca7d9459db39055b6e8a5df59",
            "1fe8efbcf05642fc95967999ff93c72b",
            "571471f7311341eb97c8a25b7b684c36",
            "dc942752643a41e1868b1ddc83ea6e76"
          ]
        },
        "id": "I-Q0Z3ASrq4r",
        "outputId": "66d32e8f-75df-47b9-eed7-df980e95dc1a"
      },
      "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": "a6c9ae7069dc42b3b590430a2ec4bc92"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset, DatasetDict\n",
        "\n",
        "dataset = DatasetDict()\n",
        "\n",
        "dataset = load_dataset(\n",
        "    \"PolyAI/minds14\", \"en-US\", split=\"train\"\n",
        ").train_test_split(train_size=450)"
      ],
      "metadata": {
        "id": "rV27EfYgrwHp"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(dataset)"
      ],
      "metadata": {
        "id": "yDtOQgwps3UW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4f9c2a58-2154-44a1-b174-fb007c6a5d29"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DatasetDict({\n",
            "    train: Dataset({\n",
            "        features: ['path', 'audio', 'transcription', 'english_transcription', 'intent_class', 'lang_id'],\n",
            "        num_rows: 450\n",
            "    })\n",
            "    test: Dataset({\n",
            "        features: ['path', 'audio', 'transcription', 'english_transcription', 'intent_class', 'lang_id'],\n",
            "        num_rows: 113\n",
            "    })\n",
            "})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = dataset.select_columns([\"audio\", \"transcription\"])"
      ],
      "metadata": {
        "id": "ID7zf1ltteAJ"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import WhisperProcessor\n",
        "\n",
        "processor = WhisperProcessor.from_pretrained(\n",
        "    \"openai/whisper-tiny\", language=\"english\", task=\"transcribe\"\n",
        ")"
      ],
      "metadata": {
        "id": "Jc4X9WuAuN1e"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[\"train\"].features"
      ],
      "metadata": {
        "id": "TFuvMRAuvNP1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ac87aee6-88b5-4f14-a8f2-4dcd95324147"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'audio': Audio(sampling_rate=8000, mono=True, decode=True, id=None),\n",
              " 'transcription': Value(dtype='string', id=None)}"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "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))"
      ],
      "metadata": {
        "id": "EuiEy9INvg5T"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def prepare_dataset(example):\n",
        "    audio = example[\"audio\"]\n",
        "\n",
        "    example = processor(\n",
        "        audio=audio[\"array\"],\n",
        "        sampling_rate=audio[\"sampling_rate\"],\n",
        "        text=example[\"transcription\"],\n",
        "    )\n",
        "    example[\"input_length\"] = len(audio[\"array\"]) / audio[\"sampling_rate\"]\n",
        "\n",
        "    return example"
      ],
      "metadata": {
        "id": "m_R3ftWUvq9f"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = dataset.map(\n",
        "    prepare_dataset, remove_columns=dataset.column_names[\"train\"], num_proc=1, batch_size=100\n",
        ")"
      ],
      "metadata": {
        "id": "aaE6DzPrvuEz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "1647bbb03b314dc3b544a45de737ea10",
            "0e4210ea38d24d6bbb150831130d02f2",
            "986170779abb4565a601d40b0d2a94bd",
            "4cb12e8aaec142eab38b83dbafd408a1",
            "cb71e4585d9d40a0a45f1fa180244e67",
            "acc31b414d754bca96d77190e8506473",
            "6bf5b4f22f364fe0827fa7ea44ba2b41",
            "80b2d86f86554cd790f46342150b4a9a",
            "bda16e862968459da13224d3448c0de1",
            "4f1ab0f3682241dd979f0094022ab066",
            "2e701964a57a47f0b23f31b0b6bc4258",
            "e886f8116c41438c896511052add1461",
            "79a51959ee214ffaba3c74b96767d6f2",
            "be9db5caca3140bd83d184e8ba15c24e",
            "c7543ccbd3754053a54bfdb14acbdccd",
            "c11f6b6e4a754cffba033f1c2853df5f",
            "e57fb9d9c0224913a9085f49cf502636",
            "2c1dd9ee2a0f43138c8142de02f6bc75",
            "e53c3596434341d9af2b0ea3365f30d0",
            "fd325c92a8c94840bdf002e9025d0ca2",
            "b3898ab8ef794b20810c6a920e39f25c",
            "414c8eb8496949188b038bf9d563699c"
          ]
        },
        "outputId": "0aec1cfd-b87d-4b67-8c80-8be059764db9"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/450 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1647bbb03b314dc3b544a45de737ea10"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/113 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e886f8116c41438c896511052add1461"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from statistics import median\n",
        "median(dataset['train']['input_length'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_daHvnqHwpZb",
        "outputId": "fe18a22b-20b6-476f-b134-03aabefc4567"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6.570625"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[\"train\"]"
      ],
      "metadata": {
        "id": "XYzm2JSn9_F2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "09b1dfaf-ea7b-4014-8f77-7bdbaeed13dd"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['input_features', 'labels', 'input_length'],\n",
              "    num_rows: 450\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "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": "oSbmQnvDwLVO"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_collator = DataCollatorSpeechSeq2SeqWithPadding(processor=processor)"
      ],
      "metadata": {
        "id": "nw7khztSw7bd"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import evaluate\n",
        "\n",
        "metric = evaluate.load(\"wer\")"
      ],
      "metadata": {
        "id": "0klV2a8Aw9FK"
      },
      "execution_count": 50,
      "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": "4cOlXceuw_nl"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import WhisperForConditionalGeneration\n",
        "\n",
        "model = WhisperForConditionalGeneration.from_pretrained(\"openai/whisper-tiny\")"
      ],
      "metadata": {
        "id": "SIMopUzDxDyQ"
      },
      "execution_count": 52,
      "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=\"english\", task=\"transcribe\", use_cache=True\n",
        ")"
      ],
      "metadata": {
        "id": "rgSqSDKuxGZF"
      },
      "execution_count": 53,
      "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-english-minds14\",  # name on the HF Hub\n",
        "    per_device_train_batch_size=16,\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=16,\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": "7QxMCqJexMFk"
      },
      "execution_count": 54,
      "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": "FiG0OVRAyd1z"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "kwargs = {\n",
        "     \"dataset_tags\": \"PolyAI/minds14\",\n",
        "    \"finetuned_from\": \"openai/whisper-tiny\",\n",
        "    \"tasks\": \"automatic-speech-recognition\",\n",
        "}"
      ],
      "metadata": {
        "id": "4fpIoj6xxSov"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "id": "evKTSE8KyYCU",
        "outputId": "543dd04d-373c-42f6-e5ce-3586d5f7f510"
      },
      "execution_count": 57,
      "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": {
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              "<IPython.core.display.HTML object>"
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              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='500' max='500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [500/500 24:51, Epoch 17/18]\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>0.339100</td>\n",
              "      <td>0.529764</td>\n",
              "      <td>0.350606</td>\n",
              "      <td>0.350775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>0.047500</td>\n",
              "      <td>0.577201</td>\n",
              "      <td>0.355989</td>\n",
              "      <td>0.355943</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>0.004600</td>\n",
              "      <td>0.652095</td>\n",
              "      <td>0.354643</td>\n",
              "      <td>0.354005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>0.001400</td>\n",
              "      <td>0.690336</td>\n",
              "      <td>0.350606</td>\n",
              "      <td>0.352067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.000900</td>\n",
              "      <td>0.709577</td>\n",
              "      <td>0.351952</td>\n",
              "      <td>0.353359</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=0.36591122806817294, metrics={'train_runtime': 1496.3336, 'train_samples_per_second': 5.346, 'train_steps_per_second': 0.334, 'total_flos': 1.9109178630144e+17, 'train_loss': 0.36591122806817294, 'epoch': 17.24})"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.push_to_hub(**kwargs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "0tujyw0uyi35",
        "outputId": "c4fa058c-1e7a-43c1-a57a-2101b0c1c0f4"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'https://huggingface.co/Aryan-401/whisper-tiny-english-minds14/tree/main/'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "s3QabA0mR7GZ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}