new_asr_eng.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/cmanny1/PGMI-youtube-analysis/blob/main/new_asr_eng.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
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        },
        "id": "WlAccXNU-7Gi",
        "outputId": "7003be4d-e4bd-4362-f429-dbaf10d42480"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sun Apr 14 23:06:32 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  NVIDIA L4                      Off | 00000000:00:03.0 Off |                    0 |\n",
            "| N/A   44C    P8              12W /  72W |      1MiB / 23034MiB |      0%      Default |\n",
            "|                                         |                      |                  N/A |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "|  No running processes found                                                           |\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "\n",
        "gpu_info = !nvidia-smi\n",
        "gpu_info = '\\n'.join(gpu_info)\n",
        "if gpu_info.find('failed') >= 0:\n",
        "  print('Not connected to a GPU')\n",
        "else:\n",
        "  print(gpu_info)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "!pip install datasets[audio]\n",
        "!pip install evaluate\n",
        "!pip install git+https://github.com/huggingface/transformers.git\n",
        "!pip install jiwer\n",
        "!pip install accelerate"
      ],
      "metadata": {
        "id": "Hr9PuV9z--OX"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install accelerate -U"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MWSadjZM_W3K",
        "outputId": "85dd4bd2-2892-4f3a-ab3d-2c126bf85f50"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.29.2)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate) (1.25.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (24.0)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate) (5.9.5)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate) (6.0.1)\n",
            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.2.1+cu121)\n",
            "Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.20.3)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.4.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.13.4)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (4.11.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.3)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2023.6.0)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (8.9.2.26)\n",
            "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.3.1)\n",
            "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (11.0.2.54)\n",
            "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (10.3.2.106)\n",
            "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (11.4.5.107)\n",
            "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.0.106)\n",
            "Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.19.3)\n",
            "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.105)\n",
            "Requirement already satisfied: triton==2.2.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.2.0)\n",
            "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.10.0->accelerate) (12.4.127)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (4.66.2)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2024.2.2)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "!pip install transformers[torch]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tBabflUJ_W_Z",
        "outputId": "e4725766-798f-4b72-8533-fb0a309db547"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers[torch] in /usr/local/lib/python3.10/dist-packages (4.40.0.dev0)\n",
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            "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch->transformers[torch]) (12.4.127)\n",
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            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[torch]) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[torch]) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[torch]) (2024.2.2)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch->transformers[torch]) (2.1.5)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch->transformers[torch]) (1.3.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162,
          "referenced_widgets": [
            "938aa959e9554101a0ce9d99e38746e8",
            "a7a477ee5ae44216a3f672769c8adf04",
            "058f74397a0341b4b864b750bf07e094",
            "566613516bb544649060a844017a93eb",
            "4de8338fd6cb4f9dadcca864d03bbcd7",
            "83a620a7b4c541b09cb4df9fbf4f636a",
            "380ca38f6f3b4656b3ca4924202cc03b",
            "4e67d59f32ce43aeb1aa5ded77438b54",
            "6018f21c31ab45fc8663fe0339eabb46",
            "eb3f995fa4454b5686d16429c971e35a",
            "6cf479ef72d84cb1aa00ab9834c30865",
            "df04aae2a0a54bb8825826b80f37514a",
            "08312d17a8b04120baa2b9456424cac6",
            "8f7ef0b68ee4471485f2d5c327f0396f",
            "5ab39e9298b5432fad2152967f4ae955",
            "f277289bcea644808a9f4a92a75a603e",
            "6352d43bf67f420f86a28147987b9be1",
            "245f44ab3ec54b84a60257011177bbb8",
            "6fcf89cc85e945868b42bd1e892786e7",
            "678a4eb80a0940ca98578a4310fed064",
            "24f4f0e5eeae4864a65abc5dbb8e12e8",
            "3baecbdd108b4743a579dd0a91a0f751",
            "e9f640bca9da49d99469675aed385c76",
            "bd0738e9adf34df4a792526a73812187",
            "96a0c11179484b63abe7280059aff778",
            "78ede23f6cb045f28ab33bb494099461",
            "9b5325dc4fb648a1a44f6b0a13c78355",
            "91f5717b205142608410322e68afdb3e",
            "e7ae7bfcf07c4dc4aa2d7ca8db2b3a6d",
            "4ce1223e31b743f59e1de0e1f49765fb",
            "2d317c92458a492b9b2d9a7eaf5862d1",
            "dca414ff163e40b49a8db3da0ae90bf6"
          ]
        },
        "id": "iS3G21wD_XCi",
        "outputId": "0c5c8ae5-1e8a-4a42-fc35-40069dceb673"
      },
      "execution_count": 5,
      "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": "938aa959e9554101a0ce9d99e38746e8"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_split_size_mb:128'\n",
        "PYTORCH_CUDA_ALLOC_CONF=expandable_segments=True"
      ],
      "metadata": {
        "id": "UUxLeI_K_XFk"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import interleave_datasets, load_dataset\n",
        "\n",
        "def load_streaming_dataset(dataset_name, dataset_config_name, split, **kwargs):\n",
        "    if \"+\" in split:\n",
        "        # load multiple splits separated by the `+` symbol *with* streaming mode\n",
        "        dataset_splits = [load_dataset(dataset_name, dataset_config_name, split=split_name, streaming=True, **kwargs) for split_name in split.split(\"+\")]\n",
        "        # interleave multiple splits to form one dataset\n",
        "        interleaved_dataset = interleave_datasets(dataset_splits)\n",
        "        return interleaved_dataset\n",
        "    else:\n",
        "        # load a single split *with* streaming mode\n",
        "        dataset = load_dataset(dataset_name, dataset_config_name, split=split, streaming=True, **kwargs)\n",
        "        return dataset\n",
        ""
      ],
      "metadata": {
        "id": "i2C8aLI3_XId"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import IterableDatasetDict\n",
        "\n",
        "raw_datasets = IterableDatasetDict()\n",
        "\n",
        "raw_datasets[\"train\"] = load_streaming_dataset(\"google/fleurs\", \"en_us\", split=\"train+validation\", use_auth_token=True)  # set split=\"train+validation\" for low-resource\n",
        "raw_datasets[\"test\"] = load_streaming_dataset(\"google/fleurs\", \"en_us\", split=\"test\", use_auth_token=True)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448,
          "referenced_widgets": [
            "410b9cb181b54d1bbb024ebbda0918e6",
            "5122eafe945846db9fd0ed73b937ebc1",
            "ac1fad3d71c94158a21f35ce9e2be223",
            "c03f7356edde4f69b9fe066f169183c5",
            "9a5b7d35b502434ea661b4ba3e6b8125",
            "6906beccf4764ea291408fbfa5a2be2b",
            "dc0ef0b8da2a47ada9f9d03a3e0f52ce",
            "631a5c85b48049449e5b8ebe2473eb2c",
            "908bb4e6cbbf45ca8b33202d8dd2ce63",
            "fa8fd359532e47adaaf407e8249de71c",
            "8fad728872d94418b97a5a802ef8a6d5",
            "3199e85646e14b548e191a53f28a4d90",
            "30781d223f3144b28bd3f84d496ac030",
            "68519fb4560a4b469abd044cd9aaf8e4",
            "ddef6dad880d4670a1bc7cafb776ae03",
            "f1bf5f9d61cf4b67a2d4a17ccb00224a",
            "65d375e66fb849dfa45b54c85863e6f2",
            "83647cc6ba544030b5f84273bf657e96",
            "1fc544efb7014bddb9aa23f278eaa07a",
            "15ccaac4acf047e2a87fed3c47ff333b",
            "a307d1e56d464addbaffee9343e644bf",
            "cf31ee47cd474737856cae6db92f1d21"
          ]
        },
        "id": "iMJFZcHj_XLC",
        "outputId": "bc6f15f5-eaa2-43a7-a1d0-63f456902afd"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/datasets/load.py:2516: FutureWarning: 'use_auth_token' was deprecated in favor of 'token' in version 2.14.0 and will be removed in 3.0.0.\n",
            "You can remove this warning by passing 'token=<use_auth_token>' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/datasets/load.py:1461: FutureWarning: The repository for google/fleurs contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/google/fleurs\n",
            "You can avoid this message in future by passing the argument `trust_remote_code=True`.\n",
            "Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/12.6k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "410b9cb181b54d1bbb024ebbda0918e6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/13.3k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3199e85646e14b548e191a53f28a4d90"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/datasets/load.py:2516: FutureWarning: 'use_auth_token' was deprecated in favor of 'token' in version 2.14.0 and will be removed in 3.0.0.\n",
            "You can remove this warning by passing 'token=<use_auth_token>' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/datasets/load.py:1461: FutureWarning: The repository for google/fleurs contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/google/fleurs\n",
            "You can avoid this message in future by passing the argument `trust_remote_code=True`.\n",
            "Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "heF6s6pL_XOq",
        "outputId": "ee13eb0b-600f-40b4-e137-f3b04bb13f96"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "IterableDatasetDict({\n",
              "    train: IterableDataset({\n",
              "        features: ['id', 'num_samples', 'path', 'audio', 'transcription', 'raw_transcription', 'gender', 'lang_id', 'language', 'lang_group_id'],\n",
              "        n_shards: 1\n",
              "    })\n",
              "    test: IterableDataset({\n",
              "        features: ['id', 'num_samples', 'path', 'audio', 'transcription', 'raw_transcription', 'gender', 'lang_id', 'language', 'lang_group_id'],\n",
              "        n_shards: 1\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_datasets = raw_datasets.remove_columns([\"id\", \"num_samples\", \"raw_transcription\", \"gender\", \"lang_id\", \"language\", \"lang_group_id\", \"path\"])"
      ],
      "metadata": {
        "id": "khZ0c7zX_XR3"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "raw_datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hv9EgoNp_XUh",
        "outputId": "b0567faf-b04b-4ad3-efbb-0ae45e7778f2"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "IterableDatasetDict({\n",
              "    train: IterableDataset({\n",
              "        features: ['audio', 'transcription'],\n",
              "        n_shards: 1\n",
              "    })\n",
              "    test: IterableDataset({\n",
              "        features: ['audio', 'transcription'],\n",
              "        n_shards: 1\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from IPython.display import display, HTML\n",
        "import random\n",
        "\n",
        "def show_random_elements(dataset, num_examples=10):\n",
        "    sample = []  # To hold sampled examples\n",
        "\n",
        "    # Try to manually fetch num_examples * 10 examples from the dataset if possible\n",
        "    try:\n",
        "        iterator = iter(dataset)\n",
        "        for _ in range(num_examples * 10):\n",
        "            sample.append(next(iterator))\n",
        "    except StopIteration:\n",
        "        # If the dataset has fewer elements than we tried to fetch, just proceed with what we have\n",
        "        pass\n",
        "\n",
        "    # Randomly select num_examples from the sampled examples, if we have enough\n",
        "    if len(sample) > num_examples:\n",
        "        picks = random.sample(sample, num_examples)\n",
        "    else:\n",
        "        # If we have less than num_examples, use all sampled examples\n",
        "        picks = sample\n",
        "\n",
        "    # Create a DataFrame to display the picked examples\n",
        "    df = pd.DataFrame(picks)\n",
        "    display(HTML(df.to_html()))\n",
        "\n",
        "# Assuming raw_datasets is a DatasetDict or similar containing your data\n",
        "# And assuming that you've correctly identified the dataset split you want to use\n",
        "# Adjust the function call as necessary, e.g., by specifying the correct dataset split like raw_datasets[\"train\"]\n",
        "show_random_elements(raw_datasets[\"train\"].remove_columns(['audio']), num_examples=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "id": "uzE03OEA_XWF",
        "outputId": "a0f3520f-2f26-4b7c-c0de-fa61663c9ae1"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>transcription</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>the amazon river is the second longest and the biggest river on earth it carries more than 8 times as much water as the second biggest river</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>the civilisation flourished in the basins of the indus river wherefore it derives its name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>these theories suggest that people have certain needs and/or desires which have been internalized as they mature to adulthood</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>if you visit the arctic or antarctic areas in the winter you will experience the polar night which means that the sun doesn't rise above the horizon</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>this theory contradicts the claim that the moon is completely devoid of geologic activity</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>although three people were inside the house when the car impacted it none of them were hurt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>visibility may also be restricted by falling or blowing snow or by condensation or ice on vehicle windows</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>massa is due to be out for at least the rest of the 2009 season</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>the east african islands are in the indian ocean off the eastern coast of africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>local authorities are warning residents in the vicinity of the plant to stay indoors turn off air-conditioners and not to drink tap water</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def prepare_dataset(batch):\n",
        "    # load and (possibly) resample audio datato 16kHz\n",
        "    audio = batch[\"audio\"]\n",
        "\n",
        "    # compute log-Mel input features from input audio array\n",
        "    batch[\"input_features\"] = processor.feature_extractor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_features[0]\n",
        "    # compute input length of audio sample in seconds\n",
        "    batch[\"input_length\"] = len(audio[\"array\"]) / audio[\"sampling_rate\"]\n",
        "\n",
        "    # optional pre-processing steps\n",
        "    transcription = batch[\"transcription\"]\n",
        "    #if do_lower_case:\n",
        "     #   transcription = transcription.lower()\n",
        "    #if do_remove_punctuation:\n",
        "      #  transcription = re.sub(punctuation_to_remove_regex, \" \", transcription).strip()\n",
        "\n",
        "    # encode target text to label ids\n",
        "    batch[\"labels\"] = processor.tokenizer(transcription).input_ids\n",
        "    return batch"
      ],
      "metadata": {
        "id": "yqmR8wqu--XS"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vectorized_datasets = raw_datasets.map(prepare_dataset, remove_columns= ['audio','transcription'])"
      ],
      "metadata": {
        "id": "z_uwXvmo--cF"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vectorized_datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k5Ay1J4c--fs",
        "outputId": "6c86a054-65f2-4041-e165-6a9a365bd8a8"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "IterableDatasetDict({\n",
              "    train: IterableDataset({\n",
              "        features: Unknown,\n",
              "        n_shards: 1\n",
              "    })\n",
              "    test: IterableDataset({\n",
              "        features: Unknown,\n",
              "        n_shards: 1\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "from dataclasses import dataclass\n",
        "from typing import Any, Dict, List, Union\n",
        "\n",
        "@dataclass\n",
        "class DataCollatorSpeechSeq2SeqWithPadding:\n",
        "    processor: Any\n",
        "\n",
        "    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:\n",
        "        # split inputs and labels since they have to be of different lengths and need different padding methods\n",
        "        # first treat the audio inputs by simply returning torch tensors\n",
        "        input_features = [{\"input_features\": feature[\"input_features\"]} for feature in features]\n",
        "        batch = self.processor.feature_extractor.pad(input_features, return_tensors=\"pt\")\n",
        "\n",
        "        # get the tokenized label sequences\n",
        "        label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n",
        "        # pad the labels to max length\n",
        "        labels_batch = self.processor.tokenizer.pad(label_features, return_tensors=\"pt\")\n",
        "\n",
        "        # replace padding with -100 to ignore loss correctly\n",
        "        labels = labels_batch[\"input_ids\"].masked_fill(labels_batch.attention_mask.ne(1), -100)\n",
        "\n",
        "        # if bos token is appended in previous tokenization step,\n",
        "        # cut bos token here as it's append later anyways\n",
        "        if (labels[:, 0] == self.processor.tokenizer.bos_token_id).all().cpu().item():\n",
        "            labels = labels[:, 1:]\n",
        "\n",
        "        batch[\"labels\"] = labels\n",
        "\n",
        "        return batch"
      ],
      "metadata": {
        "id": "h7o8w7Ab--jK"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from transformers import WhisperProcessor\n",
        "model_id = \"openai/whisper-medium\"\n",
        "processor = WhisperProcessor.from_pretrained(model_id, language=\"English\", task=\"transcribe\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310,
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        "id": "x4EMM5b_--yC",
        "outputId": "67b61f99-9d40-4b5d-b726-61b0af01a8e9"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "preprocessor_config.json:   0%|          | 0.00/185k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/283k [00:00<?, ?B/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/836k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/2.48M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "merges.txt:   0%|          | 0.00/494k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "normalizer.json:   0%|          | 0.00/52.7k [00:00<?, ?B/s]"
            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "added_tokens.json:   0%|          | 0.00/34.6k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "579e911a5ca542e2a4a9feba95833629"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/2.19k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "73acb0533b8c4f0a96800eadd6756584"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_collator = DataCollatorSpeechSeq2SeqWithPadding(processor=processor)"
      ],
      "metadata": {
        "id": "n2GcC4KODKEB"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import evaluate\n",
        "\n",
        "metric = evaluate.load(\"wer\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "3a90b205fc6e46be8111994c679c55e1",
            "c93eef61843f4988af3d1ef61ab9e078",
            "c3ab21fe79a444b9bbbd235bca261a16",
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            "328a819c0e574200ac852489a9abb212"
          ]
        },
        "id": "O9L_qckJDKMZ",
        "outputId": "321f29fa-a168-4b5e-8f45-43b7da3f1886"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/4.49k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3a90b205fc6e46be8111994c679c55e1"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluate with the 'normalised' WER\n",
        "do_normalize_eval = True\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.tokenizer.batch_decode(pred_ids, skip_special_tokens=True)\n",
        "    label_str = processor.tokenizer.batch_decode(label_ids, skip_special_tokens=True)\n",
        "\n",
        "    wer = 100 * metric.compute(predictions=pred_str, references=label_str)\n",
        "    return {\"wer\":wer}"
      ],
      "metadata": {
        "id": "9Mu0efVuDKPi"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import WhisperForConditionalGeneration\n",
        "\n",
        "model = WhisperForConditionalGeneration.from_pretrained(model_id)\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "d9ef6a2e09db4f269eb51adcdb5f9c5a",
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            "f27c5c7d9ff0408883ceb1f5fcc585fc",
            "8c1037693251463f922f0842a87e165d"
          ]
        },
        "id": "U3ivoLY5DYoD",
        "outputId": "a233133c-0097-4a35-ef93-bd0ce3c0b443"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.99k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d9ef6a2e09db4f269eb51adcdb5f9c5a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/3.06G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cf84949fbfdf49b1a4cc08cf8ed11e09"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/3.75k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d11706df5dc14b0084e88b02c402dfa4"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers.generation import GenerationConfig\n",
        "\n",
        "model.config.output_attentions = True\n",
        "model.config.output_hidden_states = True"
      ],
      "metadata": {
        "id": "q5zTtFIwDYul"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.config.forced_decoder_ids = None\n",
        "#model.config.suppress_tokens = []\n",
        "#model.config.use_cache = False"
      ],
      "metadata": {
        "id": "X86yWI22DYyF"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(torch.cuda.memory_summary(device=None, abbreviated=False))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v4DwjH5ADKT5",
        "outputId": "f2287a47-0412-4d5d-d7f4-b415f9364711"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "|===========================================================================|\n",
            "|                  PyTorch CUDA memory summary, device ID 0                 |\n",
            "|---------------------------------------------------------------------------|\n",
            "|            CUDA OOMs: 0            |        cudaMalloc retries: 0         |\n",
            "|===========================================================================|\n",
            "|        Metric         | Cur Usage  | Peak Usage | Tot Alloc  | Tot Freed  |\n",
            "|---------------------------------------------------------------------------|\n",
            "| Allocated memory      |      0 B   |      0 B   |      0 B   |      0 B   |\n",
            "|       from large pool |      0 B   |      0 B   |      0 B   |      0 B   |\n",
            "|       from small pool |      0 B   |      0 B   |      0 B   |      0 B   |\n",
            "|---------------------------------------------------------------------------|\n",
            "| Active memory         |      0 B   |      0 B   |      0 B   |      0 B   |\n",
            "|       from large pool |      0 B   |      0 B   |      0 B   |      0 B   |\n",
            "|       from small pool |      0 B   |      0 B   |      0 B   |      0 B   |\n",
            "|---------------------------------------------------------------------------|\n",
            "| Requested memory      |      0 B   |      0 B   |      0 B   |      0 B   |\n",
            "|       from large pool |      0 B   |      0 B   |      0 B   |      0 B   |\n",
            "|       from small pool |      0 B   |      0 B   |      0 B   |      0 B   |\n",
            "|---------------------------------------------------------------------------|\n",
            "| GPU reserved memory   |      0 B   |      0 B   |      0 B   |      0 B   |\n",
            "|       from large pool |      0 B   |      0 B   |      0 B   |      0 B   |\n",
            "|       from small pool |      0 B   |      0 B   |      0 B   |      0 B   |\n",
            "|---------------------------------------------------------------------------|\n",
            "| Non-releasable memory |      0 B   |      0 B   |      0 B   |      0 B   |\n",
            "|       from large pool |      0 B   |      0 B   |      0 B   |      0 B   |\n",
            "|       from small pool |      0 B   |      0 B   |      0 B   |      0 B   |\n",
            "|---------------------------------------------------------------------------|\n",
            "| Allocations           |       0    |       0    |       0    |       0    |\n",
            "|       from large pool |       0    |       0    |       0    |       0    |\n",
            "|       from small pool |       0    |       0    |       0    |       0    |\n",
            "|---------------------------------------------------------------------------|\n",
            "| Active allocs         |       0    |       0    |       0    |       0    |\n",
            "|       from large pool |       0    |       0    |       0    |       0    |\n",
            "|       from small pool |       0    |       0    |       0    |       0    |\n",
            "|---------------------------------------------------------------------------|\n",
            "| GPU reserved segments |       0    |       0    |       0    |       0    |\n",
            "|       from large pool |       0    |       0    |       0    |       0    |\n",
            "|       from small pool |       0    |       0    |       0    |       0    |\n",
            "|---------------------------------------------------------------------------|\n",
            "| Non-releasable allocs |       0    |       0    |       0    |       0    |\n",
            "|       from large pool |       0    |       0    |       0    |       0    |\n",
            "|       from small pool |       0    |       0    |       0    |       0    |\n",
            "|---------------------------------------------------------------------------|\n",
            "| Oversize allocations  |       0    |       0    |       0    |       0    |\n",
            "|---------------------------------------------------------------------------|\n",
            "| Oversize GPU segments |       0    |       0    |       0    |       0    |\n",
            "|===========================================================================|\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import torch\n",
        "torch.cuda.empty_cache()\n"
      ],
      "metadata": {
        "id": "WTWymAjlDKgS"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Seq2SeqTrainingArguments\n",
        "\n",
        "# Adjust environment for memory management, if necessary\n",
        "import os\n",
        "os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = '1'\n",
        "\n",
        "training_args = Seq2SeqTrainingArguments(\n",
        "    output_dir=\"./whisper-English\",\n",
        "    per_device_train_batch_size=2,  # Adjusted based on memory constraints\n",
        "    gradient_accumulation_steps=3,  # Increased to compensate for smaller batch size\n",
        "    learning_rate=1e-5,\n",
        "    #warmup_steps=500,\n",
        "    max_steps=200,\n",
        "    gradient_checkpointing=True,\n",
        "    fp16=True,  # Enabled for mixed precision training\n",
        "    evaluation_strategy=\"steps\",\n",
        "    per_device_eval_batch_size=3,  # Adjusted based on memory constraints\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",
        ")\n",
        "\n",
        "# Ensure your training script and environment support these changes.\n"
      ],
      "metadata": {
        "id": "GcdKwmkUD5ua"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainerCallback\n",
        "from transformers.trainer_pt_utils import IterableDatasetShard\n",
        "from torch.utils.data import IterableDataset\n",
        "\n",
        "# trainer callback to reinitialise and reshuffle the streamable datasets at the beginning of each epoch\n",
        "class ShuffleCallback(TrainerCallback):\n",
        "    def on_epoch_begin(self, args, state, control, train_dataloader, **kwargs):\n",
        "        if isinstance(train_dataloader.dataset, IterableDatasetShard):\n",
        "            pass  # set_epoch() is handled by the Trainer\n",
        "        elif isinstance(train_dataloader.dataset, IterableDataset):\n",
        "            train_dataloader.dataset.set_epoch(train_dataloader.dataset._epoch + 1)\n",
        ""
      ],
      "metadata": {
        "id": "_CIZQBDHD50B"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Seq2SeqTrainer\n",
        "\n",
        "trainer = Seq2SeqTrainer(\n",
        "    args=training_args,\n",
        "    model=model,\n",
        "    train_dataset=vectorized_datasets[\"train\"],\n",
        "    eval_dataset=vectorized_datasets[\"test\"],\n",
        "    data_collator=data_collator,\n",
        "    compute_metrics=compute_metrics,\n",
        "    tokenizer=processor,\n",
        "    #callbacks=[ShuffleCallback()],\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VIm8pDk1EH8X",
        "outputId": "2f4b5d64-d2c6-4229-996a-65c453100e00"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "max_steps is given, it will override any value given in num_train_epochs\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#model.save_pretrained(training_args.output_dir)\n",
        "processor.save_pretrained(training_args.output_dir)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7sKbpJZOEIFI",
        "outputId": "1adb149a-f6f9-4b34-8ab3-7be45cec466a"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##this empties the GPU\n",
        "torch.cuda.empty_cache()"
      ],
      "metadata": {
        "id": "ezKA11cuG95R"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418
        },
        "id": "Y2Vxf6M6EpqD",
        "outputId": "197a2dbe-ec32-44f7-e9ca-633b290aeb32"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:460: 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",
            "WhisperModel is using WhisperSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True` or `layer_head_mask` not None. Falling back to the manual attention implementation, but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation=\"eager\"` when loading the model.\n",
            "`use_cache = True` is incompatible with gradient checkpointing. Setting `use_cache = False`...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='200' max='200' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [200/200 32:20, Epoch 1/9223372036854775807]\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</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.160600</td>\n",
              "      <td>0.214907</td>\n",
              "      <td>6.818182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>0.051300</td>\n",
              "      <td>0.210583</td>\n",
              "      <td>6.622978</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Due to a bug fix in https://github.com/huggingface/transformers/pull/28687 transcription using a multilingual Whisper will default to language detection followed by transcription instead of translation to English.This might be a breaking change for your use case. If you want to instead always translate your audio to English, make sure to pass `language='en'`.\n",
            "Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.\n",
            "Non-default generation parameters: {'max_length': 448, 'suppress_tokens': [1, 2, 7, 8, 9, 10, 14, 25, 26, 27, 28, 29, 31, 58, 59, 60, 61, 62, 63, 90, 91, 92, 93, 359, 503, 522, 542, 873, 893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627, 3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647, 7273, 9061, 9383, 10428, 10929, 11938, 12033, 12331, 12562, 13793, 14157, 14635, 15265, 15618, 16553, 16604, 18362, 18956, 20075, 21675, 22520, 26130, 26161, 26435, 28279, 29464, 31650, 32302, 32470, 36865, 42863, 47425, 49870, 50254, 50258, 50358, 50359, 50360, 50361, 50362], 'begin_suppress_tokens': [220, 50257]}\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:460: 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",
            "Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.\n",
            "Non-default generation parameters: {'max_length': 448, 'suppress_tokens': [1, 2, 7, 8, 9, 10, 14, 25, 26, 27, 28, 29, 31, 58, 59, 60, 61, 62, 63, 90, 91, 92, 93, 359, 503, 522, 542, 873, 893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627, 3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647, 7273, 9061, 9383, 10428, 10929, 11938, 12033, 12331, 12562, 13793, 14157, 14635, 15265, 15618, 16553, 16604, 18362, 18956, 20075, 21675, 22520, 26130, 26161, 26435, 28279, 29464, 31650, 32302, 32470, 36865, 42863, 47425, 49870, 50254, 50258, 50358, 50359, 50360, 50361, 50362], 'begin_suppress_tokens': [220, 50257]}\n",
            "There were missing keys in the checkpoint model loaded: ['proj_out.weight'].\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=200, training_loss=0.33129685461521147, metrics={'train_runtime': 1946.8335, 'train_samples_per_second': 0.616, 'train_steps_per_second': 0.103, 'total_flos': 1.224725889024e+18, 'train_loss': 0.33129685461521147, 'epoch': 1.34})"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "kwargs = {\n",
        "    \"dataset_tags\": \"google/fleurs\",\n",
        "    \"dataset\": \"Fleurs\",  # a 'pretty' name for the training dataset\n",
        "    \"language\": \"en\",\n",
        "    \"model_name\": \"Whisper Medium English- Deraa\",  # a 'pretty' name for your model\n",
        "    \"finetuned_from\": \"steja/whisper-small-yoruba\",\n",
        "    \"tasks\": \"automatic-speech-recognition\",\n",
        "    \"tags\": \"whisper-event\",\n",
        "}\n",
        ""
      ],
      "metadata": {
        "id": "z9w6K4-JEp0i"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.push_to_hub(**kwargs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "id": "6c8YbSG1AlkS",
        "outputId": "def59abb-8d5b-40d6-e687-3d0594e97ddd"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.\n",
            "Non-default generation parameters: {'max_length': 448, 'suppress_tokens': [1, 2, 7, 8, 9, 10, 14, 25, 26, 27, 28, 29, 31, 58, 59, 60, 61, 62, 63, 90, 91, 92, 93, 359, 503, 522, 542, 873, 893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627, 3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647, 7273, 9061, 9383, 10428, 10929, 11938, 12033, 12331, 12562, 13793, 14157, 14635, 15265, 15618, 16553, 16604, 18362, 18956, 20075, 21675, 22520, 26130, 26161, 26435, 28279, 29464, 31650, 32302, 32470, 36865, 42863, 47425, 49870, 50254, 50258, 50358, 50359, 50360, 50361, 50362], 'begin_suppress_tokens': [220, 50257]}\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/deraa/whisper-English/commit/a4d6aa89a78578c29a9b19eeda80ddbdbc4fe830', commit_message='End of training', commit_description='', oid='a4d6aa89a78578c29a9b19eeda80ddbdbc4fe830', pr_url=None, pr_revision=None, pr_num=None)"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install gradio"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nql7AP_JAlsa",
        "outputId": "370677ff-f683-4fe5-ee74-a4b55a929f0d"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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            "  Downloading gradio-4.26.0-py3-none-any.whl (17.1 MB)\n",
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            "Building wheels for collected packages: ffmpy\n",
            "  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ffmpy: filename=ffmpy-0.3.2-py3-none-any.whl size=5584 sha256=245c72ece4ea89b7865908aa7a7988cf64f9fe18b8bd2e45d9c88cc246c09f6f\n",
            "  Stored in directory: /root/.cache/pip/wheels/bd/65/9a/671fc6dcde07d4418df0c592f8df512b26d7a0029c2a23dd81\n",
            "Successfully built ffmpy\n",
            "Installing collected packages: pydub, ffmpy, websockets, tomlkit, shellingham, semantic-version, ruff, python-multipart, orjson, h11, colorama, aiofiles, uvicorn, starlette, httpcore, httpx, fastapi, gradio-client, gradio\n",
            "Successfully installed aiofiles-23.2.1 colorama-0.4.6 fastapi-0.110.1 ffmpy-0.3.2 gradio-4.26.0 gradio-client-0.15.1 h11-0.14.0 httpcore-1.0.5 httpx-0.27.0 orjson-3.10.0 pydub-0.25.1 python-multipart-0.0.9 ruff-0.3.7 semantic-version-2.10.0 shellingham-1.5.4 starlette-0.37.2 tomlkit-0.12.0 uvicorn-0.29.0 websockets-11.0.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr\n",
        "\n",
        "gr.load(\"models/deraa/whisper-English\").launch()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        },
        "id": "1cLLT_QRAlyi",
        "outputId": "1e0eac43-6158-4479-a12e-924cc9a99cdb"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fetching model from: https://huggingface.co/deraa/whisper-English\n",
            "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
            "\n",
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Running on public URL: https://4acce8aa7654d902fc.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://4acce8aa7654d902fc.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Iosa4Kr6AmB0"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}