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  "cells": [
    {
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      "metadata": {
        "id": "view-in-github",
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      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hammaad2002/AdversarialAttack/blob/main/wav2vec2_0_TIMIT_finetuning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
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      "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)"
      ],
      "metadata": {
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        },
        "id": "JQBwAgr6Twbs",
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      "outputs": [
        {
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          "text": [
            "Sat Nov 26 19:07:03 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\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   57C    P8    10W /  70W |      0MiB / 15109MiB |      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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "!pip install datasets==1.18.3\n",
        "!pip install transformers==4.24.0\n",
        "!pip install jiwer\n",
        "!pip install tokenizers==0.13.2\n",
        "!pip install huggingface_hub==0.11.0"
      ],
      "metadata": {
        "id": "0e-9paYfTzUt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login, create_repo\n",
        "\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331,
          "referenced_widgets": [
            "20c6d15b645a42d2bfe0e3f4db971276",
            "e8cb4c05697b4f55ac6c864d6e5cbac3",
            "7b65cc8c50004a9da6975373ec30e5bb",
            "c09ebca22a5c45aead76d7c483a86e9c",
            "2dc0b5664e1c4d3791bcb3ab10d35da9",
            "8b843263ac3d41bcbe6f8e3c23eb6364",
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            "17c9d299f479418894fafd7e89dcdc4f",
            "458af5cff9ba4f6aa5b93d8792bbac02",
            "abb8f5bf57d04039ae51517b69fb359e",
            "2b3f6443f3c943628f82bbb246efddad",
            "a045604837b84e20b807369fefb648ba",
            "181ea56ba1dc46489cd08f04c816a943",
            "7580744d656b43ab8dac77409c4bd950",
            "d6f3a880b9bc462eaaf598bff2a11e8d",
            "864c64096ed04ae996c83d15a11762d0",
            "c442b9b2f75a464ab372aba8b0b00bae"
          ]
        },
        "id": "R2fUt0v6T2fT",
        "outputId": "57973654-069c-447f-9dde-dd1489bcb4f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Token is valid.\n",
            "Your token has been saved in your configured git credential helpers (store).\n",
            "Your token has been saved to /root/.huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "!apt install git-lfs"
      ],
      "metadata": {
        "id": "K6o4ozQ9T4oq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset, load_metric\n",
        "\n",
        "timit = load_dataset(\"timit_asr\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200,
          "referenced_widgets": [
            "25bfcc6f85404468a28ae7baa23cfd37",
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            "bfe7e2f98c6a4b1ca3f0e096d5d4cc57",
            "54f6e181bded4be3bfb585ecb764c357",
            "2fd99b8958aa4379ad22a80335dec99f",
            "a4431399e22b41f495df5d1692b0d6be",
            "0490cc6c33c34abba0346ee169221a24",
            "e4eaa7ae57d947708abbed64ecb7b203",
            "5a8479e161294bd3ac6bc44f28f49c21",
            "3aa86d1e62924271b4eb61438a26f3a4",
            "9283a8fc619d4a579093707b14737dda",
            "7081603d1e184f8eb81eb1acf6934e2d",
            "7d02153fbb3741148aa1b15e92160602",
            "47d46dbb491643f3a8c4642d3f6d18d1",
            "4a7bd5b8dce141bb9376a43e984f7384",
            "7e18bd055fc44770a47f1899928ea4a4",
            "78662ce1423d477ea157d2a119203da7",
            "5c2a741020c148a2a299a55bf4bd79d5",
            "2dff8fb9f41f4a1a8e857ce8c720ce8b",
            "04807bbc9c0042e58150c317da614193",
            "ea513ac196dc4a929363b90c46f5c957",
            "b8321a4ca5654db385f7a8180adee5ec",
            "1ad6158f02994047a133cbd867e1b60a",
            "4e68d64bf3454a74adb79adc89e586f7",
            "46c1a3d8db4146de991dff3322b421c1",
            "9c1185adc1984d3e835ae69e0e1f679b",
            "7a6901e5e5d042279bf1e0435ba609bb",
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            "8307bd9d3ed940599249babdcbfb9876",
            "9011d189230a4b80a20850061c26a8f5",
            "f881944b032c4149a52c5a9619913867",
            "24296990af64495790b4865e58de5824",
            "cec9d16d2f0e4de081776ddc72d72476",
            "f4c45336ba674d889cc7abf62a9a912a",
            "f9c14981ca0b414da6bfaa00fc64fe53",
            "838ff0a8a4fd4fa0836a418d259e5352",
            "624528b7cd294ad0952dfc84dfdab2c7",
            "813d15e988d146b69ee21f988160d5db",
            "3431459b43af44a7965215cc3db91f69",
            "b3d52c8e5ce54c28b94c120232d103b1",
            "fe2a049332fe49cc8b48159840de0584",
            "a65d2fa6ba4c48cc86c89b9bf6e95555",
            "7eb6db37cbf9480db43b583b6f20ca19",
            "24c6feb9055341a292e05103ae8c0e25",
            "7b7424c07114417abeabed712a1cdcbb",
            "f15927d37fa141ceb2cff66f38a9430c",
            "d19585b863b94d20b5051a6366374db8",
            "9fca601f1fc44aa0a78cbc6c693616f9",
            "9b80e31da5754d3ababa0041fbcc2337",
            "133c8c5c12aa4cc18bda5eb4a6820fc7",
            "9bf117d1111c4583917954c83aa58d88",
            "266b9539bee947a3b95758563c4990e9",
            "bfbc723147624efba4acf20b774b78fb",
            "f242bbc1640d47f7842758d96e9369d9",
            "eeff7fa4ffa544af897872673c208865"
          ]
        },
        "id": "bhWczBpyT6ZT",
        "outputId": "5d9dd562-932c-4d88-f8c0-348152ffe1ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/2.40k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "25bfcc6f85404468a28ae7baa23cfd37"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.06k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8fdbce4762a44dda9acea6d00fe4c728"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset timit_asr/clean (download: 828.75 MiB, generated: 7.90 MiB, post-processed: Unknown size, total: 836.65 MiB) to /root/.cache/huggingface/datasets/timit_asr/clean/2.0.1/b11b576ddcccbcefa7c9f0c4e6c2a43756f3033adffe0fb686aa61043d0450ad...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/869M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7081603d1e184f8eb81eb1acf6934e2d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1ad6158f02994047a133cbd867e1b60a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f4c45336ba674d889cc7abf62a9a912a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset timit_asr downloaded and prepared to /root/.cache/huggingface/datasets/timit_asr/clean/2.0.1/b11b576ddcccbcefa7c9f0c4e6c2a43756f3033adffe0fb686aa61043d0450ad. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7b7424c07114417abeabed712a1cdcbb"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "timit"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5d0e6XWWT8db",
        "outputId": "d2af4f0d-d0fd-4484-a53d-7c07e3856c34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['file', 'audio', 'text', 'phonetic_detail', 'word_detail', 'dialect_region', 'sentence_type', 'speaker_id', 'id'],\n",
              "        num_rows: 4620\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['file', 'audio', 'text', 'phonetic_detail', 'word_detail', 'dialect_region', 'sentence_type', 'speaker_id', 'id'],\n",
              "        num_rows: 1680\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "timit = timit.remove_columns([\"phonetic_detail\", \"word_detail\", \"dialect_region\", \"id\", \"sentence_type\", \"speaker_id\"])"
      ],
      "metadata": {
        "id": "NtFCCLUAT-ZK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import ClassLabel\n",
        "import random\n",
        "import pandas as pd\n",
        "from IPython.display import display, HTML\n",
        "\n",
        "def show_random_elements(dataset, num_examples=10):\n",
        "    assert num_examples <= len(dataset), \"Can't pick more elements than there are in the dataset.\"\n",
        "    picks = []\n",
        "    for _ in range(num_examples):\n",
        "        pick = random.randint(0, len(dataset)-1)\n",
        "        while pick in picks:\n",
        "            pick = random.randint(0, len(dataset)-1)\n",
        "        picks.append(pick)\n",
        "    \n",
        "    df = pd.DataFrame(dataset[picks])\n",
        "    display(HTML(df.to_html()))"
      ],
      "metadata": {
        "id": "nI2DBc1MUAN0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "show_random_elements(timit[\"train\"].remove_columns([\"audio\", \"file\"]), num_examples=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "QHFhTqq7UB9h",
        "outputId": "8ae494d8-ddd8-4dcf-b7d9-7c0ca686e337"
      },
      "execution_count": null,
      "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>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Did you eat yet?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Take charge of choosing her bride's maids' gowns.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Nuclear rockets can destroy airfields with ease.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Make a paste of brown sugar and mustard and spread lightly over scored surface.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Take your cobbler's shop somewhere else.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Cut a small corner off each edge.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>That pickpocket was caught red-handed.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Spring Street is straight ahead.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>The drunkard is a social outcast.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Chip postponed alimony payments until the latest possible date.</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\\"]'\n",
        "\n",
        "def remove_special_characters(batch):\n",
        "    batch[\"text\"] = re.sub(chars_to_ignore_regex, '', batch[\"text\"]).lower() + \" \"\n",
        "    return batch"
      ],
      "metadata": {
        "id": "wp_HcZTPUEDi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "timit = timit.map(remove_special_characters)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118,
          "referenced_widgets": [
            "801f4d7e837c40ff8afb80b3feca8e40",
            "ab5829588a9e438f9b00ed972656e690",
            "2cfa61f381984ba5a38ed81d00da822d",
            "7d13d6821bc54da3a12380b11f106fdb",
            "5e65026f58aa4858b5ffd4e22d0f8a06",
            "66b75699db404d129856808d4eb53c4b",
            "c883bb94a94543d7a665e5cd2f48cfac",
            "41f44892296f4b9bb468ce3582828a0f",
            "181428dd6434490a9c0a2953aaac8c46",
            "4963bbdcb5404e93947bbd5d04866f5e",
            "31b1955d041b4d0c9a7e1e3bf908b86f",
            "708a11b3808c4da7b442f3634ed601d2",
            "1f6387bbf44e41db9fefdef630cfde67",
            "69936ec174124d1784ab4efd902f8dcd",
            "da3a992f4cdb4b43bfba0c086854f6a9",
            "0a7733c721514e99929c65ac74d568bc",
            "fa31b2aa145c4739a585bf08fd20c478",
            "f7fe33245e994ac282a386bdf634e671",
            "ea7ae610df90493d9380141a1b6ab9fb",
            "f789c8d10ab04d42b211f6fccb19e8d2",
            "d76ad236501e433dafa5546885c847b0",
            "09daca7b8e8d4fe68aec0341f38c647f"
          ]
        },
        "id": "dS9mBvn-UGFC",
        "outputId": "935110b1-caeb-4637-85e5-8d3bcee6f153"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.fingerprint:Parameter 'function'=<function remove_special_characters at 0x7faee70daef0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0ex [00:00, ?ex/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "801f4d7e837c40ff8afb80b3feca8e40"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0ex [00:00, ?ex/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "708a11b3808c4da7b442f3634ed601d2"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "show_random_elements(timit[\"train\"].remove_columns([\"audio\", \"file\"]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "SBqfLFfRUHz7",
        "outputId": "2b6b979f-e92a-4435-d5e1-5273a05fad29"
      },
      "execution_count": null,
      "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>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>do you have the yellow ointment ready</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>they remained lifelong friends and companions</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>severe myopia contributed to ron's inferiority complex</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>have we not actually developed idea worship</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>growing wellkept gardens is very time consuming</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>women may never become completely equal to men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>gently place jim's foam sculpture in the box</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>we got drenched from the uninterrupted rain</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>the fat man has trouble buying life insurance or has to pay higher premiums</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>seamstresses attach zippers with a thimble needle and thread</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def extract_all_chars(batch):\n",
        "  all_text = \" \".join(batch[\"text\"])\n",
        "  vocab = list(set(all_text))\n",
        "  return {\"vocab\": [vocab], \"all_text\": [all_text]}"
      ],
      "metadata": {
        "id": "U7yGupjEUJnB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vocabs = timit.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=timit.column_names[\"train\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "4c17ffc1b8a1453cbe9c0a6c03c0006f",
            "a9bb7d78041642f393fbb91156f4230d",
            "7e339dbc39504153990d9c31477c6243",
            "76da5ecbd82b42468c1131bd27febf14",
            "4c6f39aa5ea8434490f86ec6fd50af71",
            "316f20585ed24c1db152eac58582faf9",
            "ddcebee4ac7e4deab8f0f46b060fbbfb",
            "598414762aae44d2b7cf6b51d612b88a",
            "9938aebb84064825bab0bffdef077f5e",
            "c7c88a3b90d047b4b46fa21ff99299b7",
            "ceaee59fb9544fcdaf0beff9ef149a67",
            "4e7a4f7f10974c418c22a6d54691d632",
            "07fc9e8001d04c13970888f128d6b916",
            "6758cc9b69ae4253bddf6fca96cfc0f6",
            "7f0b0b1a6d1147f8a3088408b55db963",
            "7694b11105954882965c717309744d91",
            "888043fad42c44fe8241f242d0aaf4fc",
            "3a9455f7b4ae4271b67c32bb896c24bd",
            "efc064114d6a4a5aa863609c16bab99f",
            "0eeaff6c27c44ecc8ed3b6884a292772",
            "6cf826c2dc2a4e68a1b01b0514a96473",
            "d84652a2697b4dae9600adfe20856c28"
          ]
        },
        "id": "pqJ3mp1_WZ_4",
        "outputId": "b2099f21-6cfc-41c4-df6f-61e37f5a4516"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4c17ffc1b8a1453cbe9c0a6c03c0006f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4e7a4f7f10974c418c22a6d54691d632"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vocab_list = list(set(vocabs[\"train\"][\"vocab\"][0]) | set(vocabs[\"test\"][\"vocab\"][0]))"
      ],
      "metadata": {
        "id": "S95b5DirULEL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vocab_dict = {v: k for k, v in enumerate(vocab_list)}\n",
        "vocab_dict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eaPiJrBgUQUu",
        "outputId": "a25e4233-cedf-452f-e020-e37eed553446"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'e': 0,\n",
              " 'z': 1,\n",
              " 'o': 2,\n",
              " 'q': 3,\n",
              " 'd': 4,\n",
              " 'a': 5,\n",
              " 'n': 6,\n",
              " 'b': 7,\n",
              " 'p': 8,\n",
              " 'f': 9,\n",
              " 'x': 10,\n",
              " 'r': 11,\n",
              " 'm': 12,\n",
              " 'h': 13,\n",
              " 'w': 14,\n",
              " 't': 15,\n",
              " 'l': 16,\n",
              " 'k': 17,\n",
              " 'c': 18,\n",
              " \"'\": 19,\n",
              " 'u': 20,\n",
              " 'g': 21,\n",
              " 's': 22,\n",
              " 'i': 23,\n",
              " 'j': 24,\n",
              " 'y': 25,\n",
              " 'v': 26,\n",
              " ' ': 27}"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vocab_dict[\"|\"] = vocab_dict[\" \"]\n",
        "del vocab_dict[\" \"]"
      ],
      "metadata": {
        "id": "I9rA6j2GUSu4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vocab_dict[\"[UNK]\"] = len(vocab_dict)\n",
        "vocab_dict[\"[PAD]\"] = len(vocab_dict)\n",
        "len(vocab_dict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gvjDoXQEUUrD",
        "outputId": "b61c93d5-7d60-4a08-88d1-3227932fd245"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "with open('vocab.json', 'w') as vocab_file:\n",
        "    json.dump(vocab_dict, vocab_file)"
      ],
      "metadata": {
        "id": "DGV_R77hUWMb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Wav2Vec2CTCTokenizer\n",
        "\n",
        "tokenizer = Wav2Vec2CTCTokenizer(\"./vocab.json\", unk_token=\"[UNK]\", pad_token=\"[PAD]\", word_delimiter_token=\"|\")"
      ],
      "metadata": {
        "id": "Y7CkNLXVUYCa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "repoName = \"wav2vecMyModel\""
      ],
      "metadata": {
        "id": "RPSENtqdUZ76"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "create_repo(repoName, private=False)\n",
        "tokenizer.push_to_hub(repoName)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xFYt-LEjUdjM",
        "outputId": "35eb3a9d-c147-4ec3-8b18-86b3548ff1b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/Hammad2002/wav2vecMyModel/commit/a81f54cac28cb8e101da38a414320244d4c2eab0', commit_message='Upload tokenizer', commit_description='', oid='a81f54cac28cb8e101da38a414320244d4c2eab0', pr_url=None, pr_revision=None, pr_num=None)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Wav2Vec2FeatureExtractor\n",
        "\n",
        "feature_extractor = Wav2Vec2FeatureExtractor(feature_size=1, sampling_rate=16000, padding_value=0.0, do_normalize=True, return_attention_mask=False)"
      ],
      "metadata": {
        "id": "5iuy7bD6XX22"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Wav2Vec2Processor\n",
        "\n",
        "processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)"
      ],
      "metadata": {
        "id": "f3fW8i4bXaZc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "timit[\"train\"][0][\"file\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "CP7vClb7Xh90",
        "outputId": "edcd67a9-85a0-4f08-eeb6-5dfb16dfa64c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/root/.cache/huggingface/datasets/downloads/extracted/404950a46da14eac65eb4e2a8317b1372fb3971d980d91d5d5b221275b1fd7e0/data/TRAIN/DR4/MMDM0/SI681.WAV'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "timit[\"train\"][0][\"audio\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hVfXOfJIXo53",
        "outputId": "daf947c9-26cd-4755-a8d1-3ee23a5b7d45"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'path': '/root/.cache/huggingface/datasets/downloads/extracted/404950a46da14eac65eb4e2a8317b1372fb3971d980d91d5d5b221275b1fd7e0/data/TRAIN/DR4/MMDM0/SI681.WAV',\n",
              " 'array': array([-2.1362305e-04,  6.1035156e-05,  3.0517578e-05, ...,\n",
              "        -3.0517578e-05, -9.1552734e-05, -6.1035156e-05], dtype=float32),\n",
              " 'sampling_rate': 16000}"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import IPython.display as ipd\n",
        "import numpy as np\n",
        "import random\n",
        "\n",
        "rand_int = random.randint(0, len(timit[\"train\"]))\n",
        "\n",
        "print(timit[\"train\"][rand_int][\"text\"])\n",
        "ipd.Audio(data=np.asarray(timit[\"train\"][rand_int][\"audio\"][\"array\"]), autoplay=True, rate=16000)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 93
        },
        "id": "URZSexd-XtJ1",
        "outputId": "32af8f5f-69d7-4a49-f0a4-38a9f7937b26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "don't ask me to carry an oily rag like that \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ],
            "text/html": [
              "\n",
              "                <audio  controls=\"controls\" autoplay=\"autoplay\">\n",
              "                    <source 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type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rand_int = random.randint(0, len(timit[\"train\"]))\n",
        "\n",
        "print(\"Target text:\", timit[\"train\"][rand_int][\"text\"])\n",
        "print(\"Input array shape:\", np.asarray(timit[\"train\"][rand_int][\"audio\"][\"array\"]).shape)\n",
        "print(\"Sampling rate:\", timit[\"train\"][rand_int][\"audio\"][\"sampling_rate\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OOwd6kM9Xyam",
        "outputId": "5684d8a8-e2cf-4a0f-9856-0c9dbde61655"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Target text: are holiday aprons available to us \n",
            "Input array shape: (40960,)\n",
            "Sampling rate: 16000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def prepare_dataset(batch):\n",
        "    audio = batch[\"audio\"]\n",
        "\n",
        "    # batched output is \"un-batched\" to ensure mapping is correct\n",
        "    batch[\"input_values\"] = processor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_values[0]\n",
        "    batch[\"input_length\"] = len(batch[\"input_values\"])\n",
        "    \n",
        "    with processor.as_target_processor():\n",
        "        batch[\"labels\"] = processor(batch[\"text\"]).input_ids\n",
        "    return batch"
      ],
      "metadata": {
        "id": "Dytky1iiX1Xs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "timit = timit.map(prepare_dataset, remove_columns=timit.column_names[\"train\"], num_proc=4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "id": "KWuN4y74X6_m",
        "outputId": "d5aeea10-e51c-435e-a333-ca105c05cc22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/processing_wav2vec2.py:155: UserWarning: `as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your labels by using the argument `text` of the regular `__call__` method (either in the same call as your audio inputs, or in a separate call.\n",
            "  \"`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your \"\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/processing_wav2vec2.py:155: UserWarning: `as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your labels by using the argument `text` of the regular `__call__` method (either in the same call as your audio inputs, or in a separate call.\n",
            "  \"`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your \"\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/processing_wav2vec2.py:155: UserWarning: `as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your labels by using the argument `text` of the regular `__call__` method (either in the same call as your audio inputs, or in a separate call.\n",
            "  \"`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your \"\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/processing_wav2vec2.py:155: UserWarning: `as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your labels by using the argument `text` of the regular `__call__` method (either in the same call as your audio inputs, or in a separate call.\n",
            "  \"`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your \"\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/processing_wav2vec2.py:155: UserWarning: `as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your labels by using the argument `text` of the regular `__call__` method (either in the same call as your audio inputs, or in a separate call.\n",
            "  \"`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your \"\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/processing_wav2vec2.py:155: UserWarning: `as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your labels by using the argument `text` of the regular `__call__` method (either in the same call as your audio inputs, or in a separate call.\n",
            "  \"`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your \"\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/processing_wav2vec2.py:155: UserWarning: `as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your labels by using the argument `text` of the regular `__call__` method (either in the same call as your audio inputs, or in a separate call.\n",
            "  \"`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your \"\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/processing_wav2vec2.py:155: UserWarning: `as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your labels by using the argument `text` of the regular `__call__` method (either in the same call as your audio inputs, or in a separate call.\n",
            "  \"`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your \"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_input_length_in_sec = 4.0\n",
        "timit[\"train\"] = timit[\"train\"].filter(lambda x: x < max_input_length_in_sec * processor.feature_extractor.sampling_rate, input_columns=[\"input_length\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "8e7d6789b14248118b0885e14b5995a3",
            "d4f069605333434794067d0acc1c804f",
            "a3a3fa024021429496531b7a1270be0f",
            "bb5c8c4c0a1847618f2accac62ff3927",
            "1f99e9d0d5e444b7888acd4a320b2edd",
            "9d3e6eb6cc8e43ee89597c65eafed63e",
            "60d4f8a7da40480e90c3829f58fda42f",
            "758a455ff4a54e12abb719ee82d31a8f",
            "cb26e679e5b741f19529cddc92e8af28",
            "982f27cf1d8e4978b2a675b4c84c67a1",
            "988700fd8eaa4757882561c67d3c8b89"
          ]
        },
        "id": "ibXZd93eX-_U",
        "outputId": "8d79c38e-5834-4c77-d513-4e21c5d72a49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/5 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8e7d6789b14248118b0885e14b5995a3"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "from dataclasses import dataclass, field\n",
        "from typing import Any, Dict, List, Optional, Union\n",
        "\n",
        "@dataclass\n",
        "class DataCollatorCTCWithPadding:\n",
        "    \"\"\"\n",
        "    Data collator that will dynamically pad the inputs received.\n",
        "    Args:\n",
        "        processor (:class:`~transformers.Wav2Vec2Processor`)\n",
        "            The processor used for proccessing the data.\n",
        "        padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):\n",
        "            Select a strategy to pad the returned sequences (according to the model's padding side and padding index)\n",
        "            among:\n",
        "            * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single\n",
        "              sequence if provided).\n",
        "            * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the\n",
        "              maximum acceptable input length for the model if that argument is not provided.\n",
        "            * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of\n",
        "              different lengths).\n",
        "    \"\"\"\n",
        "\n",
        "    processor: Wav2Vec2Processor\n",
        "    padding: Union[bool, str] = True\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 lenghts and need\n",
        "        # different padding methods\n",
        "        input_features = [{\"input_values\": feature[\"input_values\"]} for feature in features]\n",
        "        label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n",
        "\n",
        "        batch = self.processor.pad(\n",
        "            input_features,\n",
        "            padding=self.padding,\n",
        "            return_tensors=\"pt\",\n",
        "        )\n",
        "        with self.processor.as_target_processor():\n",
        "            labels_batch = self.processor.pad(\n",
        "                label_features,\n",
        "                padding=self.padding,\n",
        "                return_tensors=\"pt\",\n",
        "            )\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",
        "        batch[\"labels\"] = labels\n",
        "\n",
        "        return batch"
      ],
      "metadata": {
        "id": "-wbg8WUQYDFr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)"
      ],
      "metadata": {
        "id": "YY8HO3pmYFOr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "wer_metric = load_metric(\"wer\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "b08e395350de47ceaefd933b5f2b9301",
            "7cb72d0dbda544f1ad8ed01cd6abd732",
            "617e3202caa74d50b8b883087834415b",
            "50db61d2f9f64ac39d63dbba5c16b45f",
            "4a992358c3a0491f93f454800a664d20",
            "491db769e17d4dd78002982b18b1b143",
            "a2ee16abab3a4333b370f755ee2c710c",
            "8505f8dea80b428a903c3924a07cce6d",
            "3c6c57d8e9624f7ab2bcc043086b3e56",
            "5c8abb906126424093b138612a31f29c",
            "c1150e7b3e4c470a896bc2a2d25b5e2f"
          ]
        },
        "id": "40YiuR2dYHbn",
        "outputId": "81e7b5e7-c2cd-4f71-cb1d-56fb19411be0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.90k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b08e395350de47ceaefd933b5f2b9301"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_metrics(pred):\n",
        "    pred_logits = pred.predictions\n",
        "    pred_ids = np.argmax(pred_logits, axis=-1)\n",
        "\n",
        "    pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id\n",
        "\n",
        "    pred_str = processor.batch_decode(pred_ids)\n",
        "    # we do not want to group tokens when computing the metrics\n",
        "    label_str = processor.batch_decode(pred.label_ids, group_tokens=False)\n",
        "\n",
        "    wer = wer_metric.compute(predictions=pred_str, references=label_str)\n",
        "\n",
        "    return {\"wer\": wer}"
      ],
      "metadata": {
        "id": "K9pZW9qvYKTz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Wav2Vec2ForCTC\n",
        "\n",
        "model = Wav2Vec2ForCTC.from_pretrained(\n",
        "    \"facebook/wav2vec2-base\",\n",
        "    ctc_loss_reduction=\"mean\", \n",
        "    pad_token_id=processor.tokenizer.pad_token_id,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223,
          "referenced_widgets": [
            "1a87ca9710b04dda9cc170ff02b06f98",
            "cf410706775346ca8ab3de8d6b69d453",
            "f7e3a951882a4567911dffc000143d4c",
            "24928e6f474f48d0b18cfd5f01180a43",
            "8e408f57a79543ae8b2f945a653c993a",
            "ec182b69dd16445680b5629f31b738e7",
            "8472818a0cb44df3a5f6edd20d974ce5",
            "fbd78cd95953406cb3152a33061ec0e2",
            "6e7bb89943d64150a2ac49fe54de83f4",
            "89c3e165e4e7449d911f4b1f7aeda40f",
            "2b0a7a875df547fa94a3074984bd832e",
            "081d0a9ca2734b3f8ed76b5106df4eb1",
            "166999c025544f9fb5bebd7b68de54ce",
            "3904c25ca4384ada8f013bb7662f229f",
            "c64e9a40b02d4e129ac032d152d636a8",
            "3d963f7f8dcd475c998fdd79881d67ef",
            "2c5d9842d07d4281af580e5cd15b2f4a",
            "fa3167566f1d466cb543166a1999c79b",
            "8bf60048f233457096607ef2b6af1fa3",
            "64b7cf6715064e72ab60edb2720b61f4",
            "371e16334d574788aeb29ca329d1ca68",
            "d9118e60e331440d9e4712b5a46cb8ff"
          ]
        },
        "id": "1KekjxMvYMoE",
        "outputId": "6e51e947-eb83-4752-c0d6-c385c6a7878b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.84k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1a87ca9710b04dda9cc170ff02b06f98"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/configuration_utils.py:370: UserWarning: Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 Transformers. Using `model.gradient_checkpointing_enable()` instead, or if you are using the `Trainer` API, pass `gradient_checkpointing=True` in your `TrainingArguments`.\n",
            "  \"Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 \"\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/380M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "081d0a9ca2734b3f8ed76b5106df4eb1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at facebook/wav2vec2-base were not used when initializing Wav2Vec2ForCTC: ['quantizer.codevectors', 'quantizer.weight_proj.bias', 'project_hid.bias', 'project_q.bias', 'project_q.weight', 'quantizer.weight_proj.weight', 'project_hid.weight']\n",
            "- This IS expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/wav2vec2-base and are newly initialized: ['lm_head.bias', 'lm_head.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.freeze_feature_encoder()"
      ],
      "metadata": {
        "id": "1SC4QAiIYUhb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "  output_dir=repoName,\n",
        "  group_by_length=True,\n",
        "  per_device_train_batch_size=8,\n",
        "  evaluation_strategy=\"steps\",\n",
        "  num_train_epochs=30,\n",
        "  fp16=True,\n",
        "  gradient_checkpointing=True,\n",
        "  save_steps=500,\n",
        "  eval_steps=500,\n",
        "  logging_steps=500,\n",
        "  learning_rate=1e-4,\n",
        "  weight_decay=0.005,\n",
        "  warmup_steps=1000,\n",
        "  save_total_limit=2,\n",
        ")"
      ],
      "metadata": {
        "id": "1ohU77GNYWlE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Trainer\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    data_collator=data_collator,\n",
        "    args=training_args,\n",
        "    compute_metrics=compute_metrics,\n",
        "    train_dataset=timit[\"train\"],\n",
        "    eval_dataset=timit[\"test\"],\n",
        "    tokenizer=processor.feature_extractor,\n",
        ")"
      ],
      "metadata": {
        "id": "sEM0adIZYbJL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "id": "XshpYBwQYhXb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.push_to_hub()"
      ],
      "metadata": {
        "id": "wcl8ZZw-YwWk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForCTC, Wav2Vec2Processor\n",
        "\n",
        "model = AutoModelForCTC.from_pretrained(\"Hammad2002/wav2vecMyModel\")\n",
        "processor = Wav2Vec2Processor.from_pretrained(\"Hammad2002/wav2vecMyModel\")"
      ],
      "metadata": {
        "id": "V9_Wvi5hYy6r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "processor = Wav2Vec2Processor.from_pretrained(\"Hammad2002/wav2vecMyModel\")"
      ],
      "metadata": {
        "id": "QTW_c2eqZD8z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Wav2Vec2ForCTC.from_pretrained(\"Hammad2002/wav2vecMyModel\").cuda()"
      ],
      "metadata": {
        "id": "v1ZIHyNXZIuZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def map_to_result(batch):\n",
        "  with torch.no_grad():\n",
        "    input_values = torch.tensor(batch[\"input_values\"], device=\"cuda\").unsqueeze(0)\n",
        "    logits = model(input_values).logits\n",
        "\n",
        "  pred_ids = torch.argmax(logits, dim=-1)\n",
        "  batch[\"pred_str\"] = processor.batch_decode(pred_ids)[0]\n",
        "  batch[\"text\"] = processor.decode(batch[\"labels\"], group_tokens=False)\n",
        "  \n",
        "  return batch"
      ],
      "metadata": {
        "id": "EzhRTIaNZQUZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results = timit[\"test\"].map(map_to_result, remove_columns=timit[\"test\"].column_names)"
      ],
      "metadata": {
        "id": "hwTTF4g9ZSsZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Test WER: {:.3f}\".format(wer_metric.compute(predictions=results[\"pred_str\"], references=results[\"text\"])))"
      ],
      "metadata": {
        "id": "1_wY3-J6ZUl0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "show_random_elements(results)"
      ],
      "metadata": {
        "id": "BYB_SKtKZWjK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.to(\"cuda\")\n",
        "\n",
        "with torch.no_grad():\n",
        "  logits = model(torch.tensor(timit[\"test\"][:1][\"input_values\"], device=\"cuda\")).logits\n",
        "\n",
        "pred_ids = torch.argmax(logits, dim=-1)\n",
        "\n",
        "# convert ids to tokens\n",
        "\" \".join(processor.tokenizer.convert_ids_to_tokens(pred_ids[0].tolist()))"
      ],
      "metadata": {
        "id": "sK8TlDmjZagx"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}