TamilASR_Result.ipynb

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      "id": "r_Ivl7qlX0dz",
      "metadata": {
        "id": "r_Ivl7qlX0dz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 600
        },
        "outputId": "3f4de9b8-cf91-4877-b1a4-ad2872adceb4"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "nvidia-cuda-toolkit is already the newest version (11.5.1-1ubuntu1).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 49 not upgraded.\n",
            "Requirement already satisfied: bitsandbytes in /usr/local/lib/python3.10/dist-packages (0.37.0)\n",
            "Collecting bitsandbytes\n",
            "  Using cached bitsandbytes-0.44.1-py3-none-manylinux_2_24_x86_64.whl.metadata (3.5 kB)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from bitsandbytes) (2.5.0+cu121)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from bitsandbytes) (1.26.4)\n",
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            "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (1.13.1)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy==1.13.1->torch->bitsandbytes) (1.3.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch->bitsandbytes) (2.1.5)\n",
            "Using cached bitsandbytes-0.44.1-py3-none-manylinux_2_24_x86_64.whl (122.4 MB)\n",
            "Installing collected packages: bitsandbytes\n",
            "  Attempting uninstall: bitsandbytes\n",
            "    Found existing installation: bitsandbytes 0.37.0\n",
            "    Uninstalling bitsandbytes-0.37.0:\n",
            "      Successfully uninstalled bitsandbytes-0.37.0\n",
            "Successfully installed bitsandbytes-0.44.1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
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              "id": "747e2fffc38a4902a669efd9e40e1ce2"
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          },
          "metadata": {}
        }
      ],
      "source": [
        "!pip install -q transformers datasets librosa evaluate jiwer gradio bitsandbytes==0.37 accelerate\n",
        "!pip install -q git+https://github.com/huggingface/peft.git@main\n",
        "!apt-get install -y nvidia-cuda-toolkit\n",
        "!pip install bitsandbytes --upgrade\n",
        "#pip install peft\n",
        "#pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git@main"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "2kBtM9XSjKE5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2kBtM9XSjKE5",
        "outputId": "62079fa1-2709-444e-9eea-56f72e3bfca5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sun Nov 10 06:33:06 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   75C    P0              31W /  70W |    447MiB / 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",
            "+---------------------------------------------------------------------------------------+\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)\n",
        "\n",
        "import os\n",
        "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "ed0OpduhX2JF",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "dd301f323ea949dd8b36ee57f01309c5",
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            "da5b369065f94308b183f89838701cbe",
            "c3304bb0aed54e5c863a9bbea6e93365",
            "8d070729ca7b4746b58565bbcd7feb53",
            "b4d2523322a84c0e9608e8f67cae475f",
            "94f44b5abba14b34bf27190c103afe2b",
            "ff609761447c45b9ad3532af0102a6a5",
            "9317d623ae1a4dfd8ee6ba22ab25d727",
            "888e9da8f0424a8c8202bbbed7805e35",
            "7323d94bce82428a9583534f4cfba06f",
            "f2c03bccc0f142eaad38dd3a5751b2e0"
          ]
        },
        "id": "ed0OpduhX2JF",
        "outputId": "2be7ff9f-cfc3-4532-8eb9-84eeb9d1099f"
      },
      "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": "dd301f323ea949dd8b36ee57f01309c5"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()\n",
        "#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\n",
        "#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"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "XPI5OZz4u3b1",
      "metadata": {
        "id": "XPI5OZz4u3b1"
      },
      "source": [
        "# Load Dataset\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_name = \"mozilla-foundation/common_voice_13_0\"\n",
        "language_abbr = \"ta\" # Short hand code for the language we want to fine-tune"
      ],
      "metadata": {
        "id": "DilVN-4-UYtg"
      },
      "id": "DilVN-4-UYtg",
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "a2787582-554f-44ce-9f38-4180a5ed6b44",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a2787582-554f-44ce-9f38-4180a5ed6b44",
        "outputId": "7b5c36e8-ee78-4006-ee82-3e66000d11fa"
      },
      "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', 'variant'],\n",
            "        num_rows: 43350\n",
            "    })\n",
            "    test: Dataset({\n",
            "        features: ['client_id', 'path', 'audio', 'sentence', 'up_votes', 'down_votes', 'age', 'gender', 'accent', 'locale', 'segment', 'variant'],\n",
            "        num_rows: 11973\n",
            "    })\n",
            "})\n",
            "DatasetDict({\n",
            "    train: Dataset({\n",
            "        features: ['audio', 'sentence'],\n",
            "        num_rows: 43350\n",
            "    })\n",
            "    test: Dataset({\n",
            "        features: ['audio', 'sentence'],\n",
            "        num_rows: 11973\n",
            "    })\n",
            "})\n"
          ]
        }
      ],
      "source": [
        "from datasets import load_dataset, DatasetDict\n",
        "\n",
        "common_voice = DatasetDict()\n",
        "common_voice[\"train\"] = load_dataset(dataset_name, language_abbr, split=\"train\")\n",
        "common_voice[\"test\"] = load_dataset(dataset_name, language_abbr, split=\"test\")\n",
        "print(common_voice)\n",
        "\n",
        "common_voice = common_voice.remove_columns([\"accent\", \"age\", \"client_id\", \"down_votes\", \"gender\", \"locale\", \"path\", \"segment\", \"up_votes\", \"variant\"])\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": "code",
      "execution_count": 8,
      "id": "CvqyC7p9P8zr",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CvqyC7p9P8zr",
        "outputId": "03d4c0eb-cacf-42a9-9105-71352fd92b26"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "model_name_or_path = \"vasista22/whisper-tamil-small\"\n",
        "language = \"Tamil\"\n",
        "task = \"transcribe\"\n",
        "\n",
        "from transformers import WhisperFeatureExtractor\n",
        "feature_extractor = WhisperFeatureExtractor.from_pretrained(model_name_or_path)\n",
        "\n",
        "from transformers import WhisperTokenizer\n",
        "tokenizer = WhisperTokenizer.from_pretrained(model_name_or_path,language=language, task=task)\n",
        "\n",
        "from transformers import WhisperProcessor\n",
        "processor = WhisperProcessor.from_pretrained(model_name_or_path,language=language, task=task)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "381acd09-0b0f-4d04-9eb3-f028ac0e5f2c",
      "metadata": {
        "id": "381acd09-0b0f-4d04-9eb3-f028ac0e5f2c"
      },
      "source": [
        "### Prepare Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "6525c478-8962-4394-a1c4-103c54cce170",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6525c478-8962-4394-a1c4-103c54cce170",
        "outputId": "447ca39f-d7f5-4a10-f9e7-4450931eff18"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'audio': {'path': '/root/.cache/huggingface/datasets/downloads/extracted/26d01089476eefe8f1950b403fe01fb35c17249845931bb35227afa2fe442bdd/ta_train_0/common_voice_ta_26650298.mp3', 'array': array([0., 0., 0., ..., 0., 0., 0.]), 'sampling_rate': 16000}, 'sentence': 'அவரைப் பொதுமக்கள் விடாமல் பின்னாலேயே துரத்திக் கொண்டே ஓடினார்கள்.'}\n",
            "Dataset({\n",
            "    features: ['input_features', 'labels'],\n",
            "    num_rows: 43350\n",
            "})\n"
          ]
        }
      ],
      "source": [
        "from datasets import Audio\n",
        "common_voice = common_voice.cast_column(\"audio\", Audio(sampling_rate=16000))\n",
        "print(common_voice[\"train\"][0])\n",
        "\n",
        "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\n",
        "\n",
        "common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice.column_names[\"train\"], num_proc=2)\n",
        "print(common_voice[\"train\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "263a5a58-0239-4a25-b0df-c625fc9c5810",
      "metadata": {
        "id": "263a5a58-0239-4a25-b0df-c625fc9c5810"
      },
      "source": [
        "## Training and Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "8326221e-ec13-4731-bb4e-51e5fc1486c5",
      "metadata": {
        "id": "8326221e-ec13-4731-bb4e-51e5fc1486c5"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "\n",
        "from dataclasses import dataclass\n",
        "from typing import Any, Dict, List, Union\n",
        "\n",
        "\n",
        "@dataclass\n",
        "class DataCollatorSpeechSeq2SeqWithPadding:\n",
        "    processor: Any\n",
        "\n",
        "    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:\n",
        "        # split inputs and labels since they have to be of different lengths and need different padding methods\n",
        "        # first treat the audio inputs by simply returning torch tensors\n",
        "        input_features = [{\"input_features\": feature[\"input_features\"]} for feature in features]\n",
        "        batch = self.processor.feature_extractor.pad(input_features, return_tensors=\"pt\")\n",
        "\n",
        "        # get the tokenized label sequences\n",
        "        label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n",
        "        # pad the labels to max length\n",
        "        labels_batch = self.processor.tokenizer.pad(label_features, return_tensors=\"pt\")\n",
        "\n",
        "        # replace padding with -100 to ignore loss correctly\n",
        "        labels = labels_batch[\"input_ids\"].masked_fill(labels_batch.attention_mask.ne(1), -100)\n",
        "\n",
        "        # if bos token is appended in previous tokenization step,\n",
        "        # cut bos token here as it's append later anyways\n",
        "        if (labels[:, 0] == self.processor.tokenizer.bos_token_id).all().cpu().item():\n",
        "            labels = labels[:, 1:]\n",
        "\n",
        "        batch[\"labels\"] = labels\n",
        "        return batch\n",
        "\n",
        "data_collator = DataCollatorSpeechSeq2SeqWithPadding(processor=processor)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "b22b4011-f31f-4b57-b684-c52332f92890",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b22b4011-f31f-4b57-b684-c52332f92890",
        "outputId": "177ebf77-a762-40a3-b11b-d2b992a32b77"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The `load_in_4bit` and `load_in_8bit` arguments are deprecated and will be removed in the future versions. Please, pass a `BitsAndBytesConfig` object in `quantization_config` argument instead.\n"
          ]
        }
      ],
      "source": [
        "#Evaluation Metrics\n",
        "import evaluate\n",
        "metric = evaluate.load(\"wer\")\n",
        "\n",
        "#Load a Pre-Trained Checkpoint\n",
        "from transformers import WhisperForConditionalGeneration\n",
        "model = WhisperForConditionalGeneration.from_pretrained(model_name_or_path, load_in_8bit=True, device_map=\"auto\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "Cl_ZQualPt9R",
      "metadata": {
        "id": "Cl_ZQualPt9R"
      },
      "outputs": [],
      "source": [
        "#Post-processing on the model\n",
        "#8-bit model to enable training - freezing all model layers, and cast layer-norm and output layer in float32 for training and model stability.\n",
        "from peft import prepare_model_for_kbit_training\n",
        "model = prepare_model_for_kbit_training(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bmpeiajSOWCy",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bmpeiajSOWCy",
        "outputId": "f6ae16ce-19e9-4a33-ac87-f809632b9b55"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<torch.utils.hooks.RemovableHandle at 0x7a068c111780>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "#Since the Whisper model uses Convolutional layers in the Encoder,\n",
        "#checkpointing disables grad computation to avoid this we specifically need to make the inputs trainable.\n",
        "\n",
        "def make_inputs_require_grad(module, input, output):\n",
        "    output.requires_grad_(True)\n",
        "\n",
        "model.model.encoder.conv1.register_forward_hook(make_inputs_require_grad)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "DQtpDPRHPyOL",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DQtpDPRHPyOL",
        "outputId": "a850c438-2bfc-4c9d-8cae-28f07d670c81"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 3,538,944 || all params: 245,273,856 || trainable%: 1.4429\n"
          ]
        }
      ],
      "source": [
        "#Apply Low-rank adapters (LoRA) to the model\n",
        "\n",
        "from peft import LoraConfig, PeftModel, LoraModel, LoraConfig, get_peft_model\n",
        "config = LoraConfig(r=32, lora_alpha=64, target_modules=[\"q_proj\", \"v_proj\"], lora_dropout=0.05, bias=\"none\")\n",
        "model = get_peft_model(model, config) #load a PeftModel\n",
        "model.print_trainable_parameters()\n",
        "\n",
        "#We are ONLY using 1% of the total trainable parameters, thereby performing Parameter-Efficient Fine-Tuning"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2178dea4-80ca-47b6-b6ea-ba1915c90c06",
      "metadata": {
        "id": "2178dea4-80ca-47b6-b6ea-ba1915c90c06"
      },
      "source": [
        "### Define the Training Configuration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0ae3e9af-97b7-4aa0-ae85-20b23b5bcb3a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ae3e9af-97b7-4aa0-ae85-20b23b5bcb3a",
        "outputId": "6d44993a-f628-4bfe-8835-ed932f07208f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "#define all the parameters related to training\n",
        "\n",
        "from transformers import Seq2SeqTrainingArguments\n",
        "\n",
        "training_args = Seq2SeqTrainingArguments(\n",
        "    output_dir=\"reach-vb/test\",  # change to a repo name of your choice\n",
        "    per_device_train_batch_size=8,\n",
        "    gradient_accumulation_steps=1,  # increase by 2x for every 2x decrease in batch size\n",
        "    learning_rate=1e-3,\n",
        "    warmup_steps=50,\n",
        "    num_train_epochs=1,\n",
        "    evaluation_strategy=\"steps\",\n",
        "    fp16=True,\n",
        "    per_device_eval_batch_size=8,\n",
        "    generation_max_length=128,\n",
        "    logging_steps=100,\n",
        "    max_steps=100, # only for testing purposes, remove this from your final run :)\n",
        "    remove_unused_columns=False,  # required as the PeftModel forward doesn't have the signature of the wrapped model's forward\n",
        "    label_names=[\"labels\"],  # same reason as above\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d546d7fe-0543-479a-b708-2ebabec19493",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d546d7fe-0543-479a-b708-2ebabec19493",
        "outputId": "566bb91a-4cfb-40fa-d87e-95ee1f4ab2ee"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py:494: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.\n",
            "  self.scaler = torch.cuda.amp.GradScaler(**kwargs)\n",
            "max_steps is given, it will override any value given in num_train_epochs\n"
          ]
        }
      ],
      "source": [
        "from transformers import Seq2SeqTrainer, TrainerCallback, TrainingArguments, TrainerState, TrainerControl\n",
        "from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR\n",
        "\n",
        "# This callback helps to save only the adapter weights and remove the base model weights.\n",
        "class SavePeftModelCallback(TrainerCallback):\n",
        "    def on_save(\n",
        "        self,\n",
        "        args: TrainingArguments,\n",
        "        state: TrainerState,\n",
        "        control: TrainerControl,\n",
        "        **kwargs,\n",
        "    ):\n",
        "        checkpoint_folder = os.path.join(args.output_dir, f\"{PREFIX_CHECKPOINT_DIR}-{state.global_step}\")\n",
        "\n",
        "        peft_model_path = os.path.join(checkpoint_folder, \"adapter_model\")\n",
        "        kwargs[\"model\"].save_pretrained(peft_model_path)\n",
        "\n",
        "        pytorch_model_path = os.path.join(checkpoint_folder, \"pytorch_model.bin\")\n",
        "        if os.path.exists(pytorch_model_path):\n",
        "            os.remove(pytorch_model_path)\n",
        "        return control\n",
        "\n",
        "\n",
        "trainer = Seq2SeqTrainer(\n",
        "    args=training_args,\n",
        "    model=model,\n",
        "    train_dataset=common_voice[\"train\"],\n",
        "    eval_dataset=common_voice[\"test\"],\n",
        "    data_collator=data_collator,\n",
        "    # compute_metrics=compute_metrics,\n",
        "    tokenizer=processor.feature_extractor,\n",
        "    callbacks=[SavePeftModelCallback],\n",
        ")\n",
        "model.config.use_cache = False  # silence the warnings. Please re-enable for inference!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ee8b7b8e-1c9a-4d77-9137-1778a629e6de",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "ee8b7b8e-1c9a-4d77-9137-1778a629e6de",
        "outputId": "0c041b31-74ed-4df9-b548-8d649204d810"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The `run_name` is currently set to the same value as `TrainingArguments.output_dir`. If this was not intended, please specify a different run name by setting the `TrainingArguments.run_name` parameter.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Using wandb-core as the SDK backend. Please refer to https://wandb.me/wandb-core for more information.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "        window._wandbApiKey = new Promise((resolve, reject) => {\n",
              "            function loadScript(url) {\n",
              "            return new Promise(function(resolve, reject) {\n",
              "                let newScript = document.createElement(\"script\");\n",
              "                newScript.onerror = reject;\n",
              "                newScript.onload = resolve;\n",
              "                document.body.appendChild(newScript);\n",
              "                newScript.src = url;\n",
              "            });\n",
              "            }\n",
              "            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n",
              "            const iframe = document.createElement('iframe')\n",
              "            iframe.style.cssText = \"width:0;height:0;border:none\"\n",
              "            document.body.appendChild(iframe)\n",
              "            const handshake = new Postmate({\n",
              "                container: iframe,\n",
              "                url: 'https://wandb.ai/authorize'\n",
              "            });\n",
              "            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n",
              "            handshake.then(function(child) {\n",
              "                child.on('authorize', data => {\n",
              "                    clearTimeout(timeout)\n",
              "                    resolve(data)\n",
              "                });\n",
              "            });\n",
              "            })\n",
              "        });\n",
              "    "
            ]
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
            "wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit:"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " ··········\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Tracking run with wandb version 0.18.5"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20241110_022605-ybdipvtw</code>"
            ]
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/ssncollege/huggingface/runs/ybdipvtw' target=\"_blank\">reach-vb/test</a></strong> to <a href='https://wandb.ai/ssncollege/huggingface' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View project at <a href='https://wandb.ai/ssncollege/huggingface' target=\"_blank\">https://wandb.ai/ssncollege/huggingface</a>"
            ]
          },
          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run at <a href='https://wandb.ai/ssncollege/huggingface/runs/ybdipvtw' target=\"_blank\">https://wandb.ai/ssncollege/huggingface/runs/ybdipvtw</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:632: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.5 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  return fn(*args, **kwargs)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:316: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
            "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='100' max='100' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [100/100 51:28, Epoch 0/1]\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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.141300</td>\n",
              "      <td>0.195643</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=100, training_loss=0.1413337230682373, metrics={'train_runtime': 3350.6723, 'train_samples_per_second': 0.239, 'train_steps_per_second': 0.03, 'total_flos': 2.34945183744e+17, 'train_loss': 0.1413337230682373, 'epoch': 0.018453589223103892})"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0576aa2a",
      "metadata": {
        "id": "0576aa2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118,
          "referenced_widgets": [
            "74af30bf83ef456a8753d31968ed4c0e",
            "9f97297eb45f49d9b46cd652511eb277",
            "dc4555028c5c4f978a5c6aa438ac5d2e",
            "ed7146860f86464d8c7067ca75e74054",
            "d9e699ff335a45a98ed2abf9dfd503f5",
            "953807ada0a84562b25c480e8b5afeb9",
            "481cfe1ec1574447bf6fec0f8fb51a1c",
            "f3064d5f111440b28b73663b70145ac6",
            "5c3d5aaae2144c4eafd8929d3a2c9059",
            "0afb0be7c3d34a78a2b4487ab6ee6412",
            "41ed8b8636c04f3aadd4285fc721d245"
          ]
        },
        "outputId": "0ab101bb-7934-487c-e68f-8e7ac4dcaba0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "adapter_model.safetensors:   0%|          | 0.00/14.2M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "74af30bf83ef456a8753d31968ed4c0e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/Syed-Azim-7/whisper-small-Tamil-ASR/commit/0ca48e115c6b324a7ff94908fb73bad23df1592d', commit_message='Upload model', commit_description='', oid='0ca48e115c6b324a7ff94908fb73bad23df1592d', pr_url=None, repo_url=RepoUrl('https://huggingface.co/Syed-Azim-7/whisper-small-Tamil-ASR', endpoint='https://huggingface.co', repo_type='model', repo_id='Syed-Azim-7/whisper-small-Tamil-ASR'), pr_revision=None, pr_num=None)"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "#Model is fine-tuned, push model on to Hugging Face Hub, this will later help us directly infer the model from model repo.\n",
        "\n",
        "peft_model_id = \"Syed-Azim-7/whisper-small-Tamil-ASR\"\n",
        "model.push_to_hub(peft_model_id)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "SlyyOGnPgi_I",
      "metadata": {
        "id": "SlyyOGnPgi_I"
      },
      "source": [
        "# Evaluation and Inference"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "273a996c",
      "metadata": {
        "id": "273a996c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fa490dbc-db07-4c87-d22b-6ab156c15790"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The `load_in_4bit` and `load_in_8bit` arguments are deprecated and will be removed in the future versions. Please, pass a `BitsAndBytesConfig` object in `quantization_config` argument instead.\n"
          ]
        }
      ],
      "source": [
        "---from peft import PeftModel, PeftConfig\n",
        "from transformers import WhisperForConditionalGeneration, Seq2SeqTrainer\n",
        "\n",
        "peft_model_id = \"Syed-Azim-7/whisper-small-Tamil-ASR\" # Use the same model ID as before.\n",
        "peft_config = PeftConfig.from_pretrained(peft_model_id)\n",
        "model = WhisperForConditionalGeneration.from_pretrained(\n",
        "    peft_config.base_model_name_or_path, load_in_8bit=True, device_map=\"auto\"\n",
        ")\n",
        "model = PeftModel.from_pretrained(model, peft_model_id)\n",
        "model.config.use_cache = True"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "401ceaa6",
      "metadata": {
        "id": "401ceaa6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d1f70597-7f0b-4ef7-b163-e3d1bb0e23e9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r  0%|          | 0/150 [00:00<?, ?it/s]<ipython-input-14-a6c43cd5c39e>:16: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
            "  with torch.cuda.amp.autocast():\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1375: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use and modify the model generation configuration (see https://huggingface.co/docs/transformers/generation_strategies#default-text-generation-configuration )\n",
            "  warnings.warn(\n",
            "The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:316: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
            "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n",
            "100%|██████████| 150/150 [40:14<00:00, 16.10s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "wer=39.44160440424695\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "from torch.utils.data import DataLoader,Subset\n",
        "from tqdm import tqdm\n",
        "import numpy as np\n",
        "import gc\n",
        "\n",
        "test_subset_size = int(len(common_voice[\"test\"]) * 0.1)\n",
        "test_indices = np.random.choice(len(common_voice[\"test\"]), test_subset_size, replace=False)\n",
        "test_subset = Subset(common_voice[\"test\"], test_indices)\n",
        "\n",
        "eval_dataloader = DataLoader(test_subset, batch_size=8, collate_fn=data_collator)\n",
        "\n",
        "#eval_dataloader = DataLoader(common_voice[\"test\"], batch_size=8, collate_fn=data_collator)\n",
        "\n",
        "model.eval()\n",
        "for step, batch in enumerate(tqdm(eval_dataloader)):\n",
        "    with torch.cuda.amp.autocast():\n",
        "        with torch.no_grad():\n",
        "            generated_tokens = (\n",
        "                model.generate(\n",
        "                    input_features=batch[\"input_features\"].to(\"cuda\"),\n",
        "                    decoder_input_ids=batch[\"labels\"][:, :4].to(\"cuda\"),\n",
        "                    max_new_tokens=255,\n",
        "                )\n",
        "                .cpu()\n",
        "                .numpy()\n",
        "            )\n",
        "            labels = batch[\"labels\"].cpu().numpy()\n",
        "            labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
        "            decoded_preds = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)\n",
        "            decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
        "            metric.add_batch(\n",
        "                predictions=decoded_preds,\n",
        "                references=decoded_labels,\n",
        "            )\n",
        "    del generated_tokens, labels, batch\n",
        "    gc.collect()\n",
        "wer = 100 * metric.compute()\n",
        "print(f\"{wer=}\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import torch\n",
        "import gradio as gr\n",
        "from transformers import (\n",
        "    AutomaticSpeechRecognitionPipeline,\n",
        "    WhisperForConditionalGeneration,\n",
        "    WhisperTokenizer,\n",
        "    WhisperProcessor,\n",
        ")\n",
        "from peft import PeftModel, PeftConfig\n",
        "\n",
        "\n",
        "peft_model_id = \"smangrul/openai-whisper-large-v2-LORA-colab\"\n",
        "language = \"Marathi\"\n",
        "task = \"transcribe\"\n",
        "peft_config = PeftConfig.from_pretrained(peft_model_id)\n",
        "model = WhisperForConditionalGeneration.from_pretrained(\n",
        "    peft_config.base_model_name_or_path, quantization_config=BitsAndBytesConfig(load_in_8bit=True), device_map=\"auto\"\n",
        ")\n",
        "\n",
        "model = PeftModel.from_pretrained(model, peft_model_id)\n",
        "tokenizer = WhisperTokenizer.from_pretrained(peft_config.base_model_name_or_path, language=language, task=task)\n",
        "processor = WhisperProcessor.from_pretrained(peft_config.base_model_name_or_path, language=language, task=task)\n",
        "feature_extractor = processor.feature_extractor\n",
        "forced_decoder_ids = processor.get_decoder_prompt_ids(language=language, task=task)\n",
        "pipe = AutomaticSpeechRecognitionPipeline(model=model, tokenizer=tokenizer, feature_extractor=feature_extractor)\n",
        "\n",
        "\n",
        "def transcribe(audio):\n",
        "    with torch.cuda.amp.autocast():\n",
        "        text = pipe(audio, generate_kwargs={\"forced_decoder_ids\": forced_decoder_ids}, max_new_tokens=255)[\"text\"]\n",
        "    return text\n",
        "\n",
        "\n",
        "iface = gr.Interface(\n",
        "    fn=transcribe,\n",
        "    inputs=gr.Audio(source=\"microphone\", type=\"filepath\"),\n",
        "    outputs=\"text\",\n",
        "    title=\"PEFT LoRA + INT8 Whisper Large V2 Marathi\",\n",
        "    description=\"Realtime demo for Marathi speech recognition using `PEFT-LoRA+INT8` fine-tuned Whisper Large V2 model.\",\n",
        ")\n",
        "\n",
        "iface.launch(share=True)"
      ],
      "metadata": {
        "id": "VU4UK2l5HDrP"
      },
      "id": "VU4UK2l5HDrP",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sBWp9gU_b6Iw"
      },
      "id": "sBWp9gU_b6Iw",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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