GSoC_Wav2Vec_Tutorial.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/PrakritiShetty/GSoC2024-Overhaul_of_AuToBI/blob/main/GSoC_Wav2Vec_Tutorial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DxPEwsGstYIl"
      },
      "source": [
        "\n",
        "\n",
        "*   Finetune Wav2Vec2 on the MInDS-14 dataset to classify speaker intent.\n",
        "*   Use the finetuned model for inference.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "JwqBpDel9Whf",
        "outputId": "13bd17b8-323c-4a31-8e61-68dd76db01cc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.41.2)\n",
            "Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (2.19.2)\n",
            "Requirement already satisfied: evaluate in /usr/local/lib/python3.10/dist-packages (0.4.2)\n",
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            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.25.2)\n",
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            "Requirement already satisfied: multiprocess in /usr/local/lib/python3.10/dist-packages (from datasets) (0.70.16)\n",
            "Requirement already satisfied: fsspec[http]<=2024.3.1,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2023.6.0)\n",
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            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.4.1)\n",
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            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.9.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.0->transformers) (4.12.1)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.6.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.4)\n",
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)\n"
          ]
        }
      ],
      "source": [
        "pip install transformers datasets evaluate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332,
          "referenced_widgets": [
            "d19070504c0840fb956153cd905ae47e",
            "65514d96fbd74a84a66b6feb76631dc8",
            "d2f31ac50aa8498d9d4e878d5a929223",
            "b6e0d7a1776440f194bd19bc64ca1020",
            "928b65fc4ae341e486ecf0145fbd0c0b",
            "b94265d6251c440a92379016c3e2ec22",
            "d2ea0e4df22144a9bcfbcf9a747bd50c",
            "e7badce38dde4ed0badd715f1daec2d0",
            "9c4647de32ea4057957128315c6af302",
            "999687d06afc4a25945d5c54e6ca1bf2",
            "36cd39719a32499eaadcebda48763d37",
            "945bb5ec78254564a9c312f70560123a",
            "9a3e63fb3223482ba68510267bc40e85",
            "257838fa1a8a43b3b026b103901e5a63",
            "03ac90bc59ce4273a766b9d0f43598a6",
            "752275422d5e4123ae98f07d03a4cb02",
            "e190644f2128471bbabba3a76f097cdd"
          ]
        },
        "collapsed": true,
        "id": "eFLTK1IvhBSE",
        "outputId": "6be5036d-e1e7-4c18-a7ab-327bae118ac8"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d19070504c0840fb956153cd905ae47e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Weisbl34r3cc"
      },
      "source": [
        "# Load the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "CRjO1v0bhG21",
        "outputId": "2b3fe7d2-a9f7-4ab5-c121-cb5b7997cdb5"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: 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:1491: FutureWarning: The repository for PolyAI/minds14 contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/PolyAI/minds14\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"
          ]
        }
      ],
      "source": [
        "from datasets import load_dataset, Audio\n",
        "\n",
        "minds = load_dataset(\"PolyAI/minds14\", name=\"en-US\", split=\"train\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4x2LMv5mlTXO"
      },
      "outputs": [],
      "source": [
        "minds = minds.train_test_split(test_size=0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-x3vZ5rgqqIi",
        "outputId": "ba38ea4e-0665-4d77-859b-3f0d60e832bd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['path', 'audio', 'transcription', 'english_transcription', 'intent_class', 'lang_id'],\n",
              "        num_rows: 450\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['path', 'audio', 'transcription', 'english_transcription', 'intent_class', 'lang_id'],\n",
              "        num_rows: 113\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# visualise the dataset\n",
        "minds"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DGcm-8eNqtZ-"
      },
      "outputs": [],
      "source": [
        "minds = minds.remove_columns([\"path\", \"transcription\", \"english_transcription\", \"lang_id\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xLBihgW-q2og",
        "outputId": "a25f9f57-01ab-4610-97b1-77891eb9ca49"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['audio', 'intent_class'],\n",
              "    num_rows: 450\n",
              "})"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "minds[\"train\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NDrQrlUkq5zW",
        "outputId": "37fce995-4767-4aff-b966-841f0769f53a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'audio': {'path': '/root/.cache/huggingface/datasets/downloads/extracted/28aa727f91fee90575c34956bab09d1716cfaf460c6afcba86a10f04a7d58b83/en-US~BUSINESS_LOAN/602b987c963e11ccd901cb9f.wav',\n",
              "  'array': array([ 0.        ,  0.        ,  0.00024414, ...,  0.00024414,\n",
              "         -0.00024414,  0.00024414]),\n",
              "  'sampling_rate': 8000},\n",
              " 'intent_class': 5}"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "minds['train'][449] # can check every datapoint from 0 to 449"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HHACgYBYsw8e"
      },
      "source": [
        "To make it easier for the model to get the label name from the label id:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JdRv5WGSq-a1",
        "outputId": "89a30957-15be-4407-df3b-21b90b7b827b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['abroad',\n",
              " 'address',\n",
              " 'app_error',\n",
              " 'atm_limit',\n",
              " 'balance',\n",
              " 'business_loan',\n",
              " 'card_issues',\n",
              " 'cash_deposit',\n",
              " 'direct_debit',\n",
              " 'freeze',\n",
              " 'high_value_payment',\n",
              " 'joint_account',\n",
              " 'latest_transactions',\n",
              " 'pay_bill']"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "labels = minds[\"train\"].features[\"intent_class\"]\n",
        "labels\n",
        "labels = labels.names\n",
        "labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "muSS7QV2shy7"
      },
      "outputs": [],
      "source": [
        "label2id, id2label = dict(), dict()\n",
        "for i,label in enumerate(labels):\n",
        "  label2id[label] = str(i)\n",
        "  id2label[str(i)] = label"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "-69Cg-jwtCrz",
        "outputId": "a65f5c8f-2bba-4b0b-8a62-d8c87c9a1a57"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'abroad'"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "id2label[str(0)]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tL2hIyvAtJhi"
      },
      "source": [
        "# Preprocess the dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WTjGsti7tQBM"
      },
      "source": [
        "Feature Extractor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R5a7HHT4tHHb",
        "outputId": "263ce728-cfa6-4a19-e291-57a5c58d8e9b"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py:364: 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",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoFeatureExtractor\n",
        "\n",
        "# feature_extractor = AutoFeatureExtractor.from_pretrained(\"microsoft/wavlm-base-plus-sd\")\n",
        "feature_extractor = AutoFeatureExtractor.from_pretrained(\"facebook/wav2vec2-base\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NFHRUdi1EONo"
      },
      "source": [
        "Sampling Rate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FTG3v_ZM87YU",
        "outputId": "33a26cef-f409-40d5-b40e-17347ef826e3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "8000"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "minds['train'].features\n",
        "sampling_rate  = minds[\"train\"].features[\"audio\"].sampling_rate\n",
        "sampling_rate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E6FaY2EPGVNW",
        "outputId": "2b1f3865-fa76-4e5b-ad09-7faf6a868bbe"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'audio': {'path': '/root/.cache/huggingface/datasets/downloads/extracted/28aa727f91fee90575c34956bab09d1716cfaf460c6afcba86a10f04a7d58b83/en-US~HIGH_VALUE_PAYMENT/602b9e9d963e11ccd901cc28.wav',\n",
              "  'array': array([-8.59331340e-06, -1.41524244e-04, -2.34635081e-04, ...,\n",
              "          8.24356824e-03,  2.36976370e-02,  1.74179655e-02]),\n",
              "  'sampling_rate': 16000},\n",
              " 'intent_class': 10}"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "minds = minds.cast_column(\"audio\", Audio(sampling_rate=16_000))\n",
        "dataset = minds['train']\n",
        "dataset[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zGqdUfJrEQg7"
      },
      "source": [
        "Preprocessing Function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Etjw_AJHRNA",
        "outputId": "42d87464-8532-4e07-e9be-735a76a181aa"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "16000"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "feature_extractor.sampling_rate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G2yIqCZtDeXl"
      },
      "outputs": [],
      "source": [
        "def preprocess_function(examples):\n",
        "\n",
        "  audio_arrays = [x[\"array\"] for x in examples[\"audio\"]]\n",
        "\n",
        "  inputs = feature_extractor(\n",
        "      audio_arrays,\n",
        "      sampling_rate = feature_extractor.sampling_rate,\n",
        "      max_length = 16000,\n",
        "      truncation = True\n",
        "  )\n",
        "\n",
        "  return inputs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VpaqOR8HRoUm"
      },
      "source": [
        "Preprocess!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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            "fa46a50798ee479eaf948a2b3cc52e2a",
            "de230da240bd4688a49007bea1d3c8e0",
            "f07a37b0a3d641d3a1f14a47166be20d",
            "e688019ff5e642509865dc3bf24cd54f",
            "01b109e282cc463bb67cc5c54668defe",
            "cc1695e538b340c5bb7284dd2926e854",
            "d459144fb68249339ac0851d799b10c9",
            "27ea1084e6c542679820f0230f607e14",
            "f96c20d4ee434a65bcb1e1b52192bbc8",
            "fcf75791b70a4898b7a8dc5dce61b3b2",
            "258a37ebb97f474c81edbc8444f465ef",
            "a204f8bc1dba4ddba41c738d18f72f56",
            "25ae5ab0b26d46e9b71e8e501ac4b36f",
            "4168ba4209ca4ffd851264290d5ccec6",
            "661fb479ef8144f99fd95d538df97164",
            "68a58101b5014b0fbb7ca987ddce42a3",
            "a59c30d30173488d9c4d3f874ac64399",
            "a2eeb5db0641458e80f8ee27f81d133a",
            "3e0963029ee94d258f3b8aaa73a0b2ab"
          ]
        },
        "collapsed": true,
        "id": "wTHAzjXJG_0p",
        "outputId": "cef7faab-0a56-4fe1-add4-17d3852528ca"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ca6e94e34bf54df28e932ec6a1d3f44b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/450 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f96c20d4ee434a65bcb1e1b52192bbc8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/113 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# to apply the preprocessing function over the entire dataset, use the map function\n",
        "encoded_minds = minds.map(\n",
        "    preprocess_function,\n",
        "    remove_columns = \"audio\",\n",
        "    batched = True\n",
        "    )\n",
        "\n",
        "encoded_minds = encoded_minds.rename_column(\"intent_class\",\"label\")\n",
        "\n",
        "# encoded_minds[\"train\"][0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9d4hTmJ4RqQv"
      },
      "source": [
        "Evaluation metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Wr1l32rrRNJ2"
      },
      "outputs": [],
      "source": [
        "import evaluate\n",
        "\n",
        "accuracy = evaluate.load(\"accuracy\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IhZ7HgqIRxik"
      },
      "outputs": [],
      "source": [
        "import numpy as np"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eSnpbLNqSOcE"
      },
      "source": [
        "Evaluation function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w2SWBpziR0vo"
      },
      "outputs": [],
      "source": [
        "def compute_metrics(eval_pred):\n",
        "  predictions = np.argmax( eval_pred.predictions, axis = 1)\n",
        "  return accuracy.compute(\n",
        "      predictions = predictions,\n",
        "      references = eval_pred.label_ids\n",
        "  )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tLDf_xYGSYF0"
      },
      "source": [
        "# Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z5MqaxDoT6No"
      },
      "source": [
        "Training function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c1CP_ZdxSMRp",
        "outputId": "36129180-f7fc-462b-de71-50f9d11d9069"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py:364: 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",
            "  warnings.warn(\n",
            "Some weights of Wav2Vec2ForSequenceClassification were not initialized from the model checkpoint at facebook/wav2vec2-base and are newly initialized: ['classifier.bias', 'classifier.weight', 'projector.bias', 'projector.weight', 'wav2vec2.encoder.pos_conv_embed.conv.parametrizations.weight.original0', 'wav2vec2.encoder.pos_conv_embed.conv.parametrizations.weight.original1']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoModelForAudioClassification, TrainingArguments, Trainer\n",
        "\n",
        "num_labels = len(id2label)\n",
        "\n",
        "model = AutoModelForAudioClassification.from_pretrained(\n",
        "    \"facebook/wav2vec2-base\",\n",
        "    num_labels = num_labels,\n",
        "    label2id = label2id,\n",
        "    id2label = id2label,\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "En_0Q-MQT9oV"
      },
      "source": [
        "Training hyperparameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "XVobcKNwXznh",
        "outputId": "299ee234-d29d-45dc-d921-a408ebaf8d4f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.31.0)\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) (2.3.0+cu121)\n",
            "Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.23.2)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.4.3)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.14.0)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (4.12.1)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.12.1)\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) (3.1.4)\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==2.20.5 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.20.5)\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==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.3.0)\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) (12.5.40)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (2.32.3)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (4.66.4)\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) (3.7)\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) (2024.6.2)\n",
            "Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n"
          ]
        }
      ],
      "source": [
        "! pip install accelerate -U"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "duSg7xjlZB20",
        "outputId": "277b0b7a-eb0f-4ced-8190-3b48533dcd05"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: transformers[torch] in /usr/local/lib/python3.10/dist-packages (4.41.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (3.14.0)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (0.23.2)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (1.25.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (24.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (2024.5.15)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (2.32.3)\n",
            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (0.19.1)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (0.4.3)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (4.66.4)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (2.3.0+cu121)\n",
            "Requirement already satisfied: accelerate>=0.21.0 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (0.31.0)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate>=0.21.0->transformers[torch]) (5.9.5)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.0->transformers[torch]) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.0->transformers[torch]) (4.12.1)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch->transformers[torch]) (1.12.1)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch->transformers[torch]) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch->transformers[torch]) (3.1.4)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->transformers[torch]) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->transformers[torch]) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->transformers[torch]) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch->transformers[torch]) (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->transformers[torch]) (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->transformers[torch]) (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->transformers[torch]) (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->transformers[torch]) (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->transformers[torch]) (12.1.0.106)\n",
            "Requirement already satisfied: nvidia-nccl-cu12==2.20.5 in /usr/local/lib/python3.10/dist-packages (from torch->transformers[torch]) (2.20.5)\n",
            "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->transformers[torch]) (12.1.105)\n",
            "Requirement already satisfied: triton==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torch->transformers[torch]) (2.3.0)\n",
            "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch->transformers[torch]) (12.5.40)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[torch]) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[torch]) (3.7)\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]) (2024.6.2)\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<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch->transformers[torch]) (1.3.0)\n"
          ]
        }
      ],
      "source": [
        "! pip install transformers[torch]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "c5_bpkfbZr5B",
        "outputId": "02ec6437-10b1-4c3f-e676-14f3f7215a17"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.31.0)\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) (2.3.0+cu121)\n",
            "Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.23.2)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.4.3)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.14.0)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (4.12.1)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.12.1)\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) (3.1.4)\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==2.20.5 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.20.5)\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==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.3.0)\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) (12.5.40)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (2.32.3)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (4.66.4)\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) (3.7)\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) (2024.6.2)\n",
            "Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n"
          ]
        }
      ],
      "source": [
        "! pip install accelerate --upgrade"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iEHP4F0jVCcw",
        "outputId": "11ecf878-8f6b-4ec0-923f-21811f0aaf31"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1474: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "training_args = TrainingArguments(\n",
        "    output_dir = \"GSoC_wav2vec_DetectionOfIntonationalUnits\",\n",
        "    evaluation_strategy = \"epoch\",\n",
        "    save_strategy = \"epoch\",\n",
        "    learning_rate = 3e-5,\n",
        "    per_device_train_batch_size = 32,\n",
        "    gradient_accumulation_steps = 4,\n",
        "    per_device_eval_batch_size = 32,\n",
        "    num_train_epochs = 10,\n",
        "    warmup_ratio = 0.1,\n",
        "    logging_steps = 10,\n",
        "    load_best_model_at_end = True,\n",
        "    metric_for_best_model = \"accuracy\",\n",
        "    push_to_hub = True\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2by1EdcnVylt"
      },
      "outputs": [],
      "source": [
        "trainer = Trainer(\n",
        "    model = model,\n",
        "    args = training_args,\n",
        "    train_dataset = encoded_minds[\"train\"],\n",
        "    eval_dataset = encoded_minds[\"test\"],\n",
        "    tokenizer = feature_extractor,\n",
        "    compute_metrics = compute_metrics,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 263
        },
        "id": "t2gIUPXDWQdX",
        "outputId": "6a269e59-080b-418c-8c3f-6466f0706868"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='30' max='30' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [30/30 1:26:11, Epoch 8/10]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>No log</td>\n",
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              "      <td>0.070796</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>2.640547</td>\n",
              "      <td>0.097345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
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              "      <td>2.657977</td>\n",
              "      <td>0.053097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>2.634100</td>\n",
              "      <td>2.660881</td>\n",
              "      <td>0.070796</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
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              "      <td>2.665167</td>\n",
              "      <td>0.070796</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>2.616200</td>\n",
              "      <td>2.665798</td>\n",
              "      <td>0.044248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>2.613800</td>\n",
              "      <td>2.666549</td>\n",
              "      <td>0.044248</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
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          },
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              "TrainOutput(global_step=30, training_loss=2.6213550567626953, metrics={'train_runtime': 5371.8028, 'train_samples_per_second': 0.838, 'train_steps_per_second': 0.006, 'total_flos': 3.26841433344e+16, 'train_loss': 2.6213550567626953, 'epoch': 8.0})"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "referenced_widgets": [
            "bffd46992b1d458ea596f3cbf802388b"
          ]
        },
        "id": "XkT3KXdaWYn_",
        "outputId": "a730542a-48f6-4168-bd56-44bd06d24723"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bffd46992b1d458ea596f3cbf802388b",
              "version_major": 2,
              "version_minor": 0
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            "text/plain": [
              "events.out.tfevents.1717825209.463da12eb84f.14749.0:   0%|          | 0.00/10.8k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
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            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/prakritishetty/GSoC_wavLM_DetectionOfIntonationalUnits/commit/1bd02a404e9678ccc8bcdfadf5351b3e054b94b8', commit_message='End of training', commit_description='', oid='1bd02a404e9678ccc8bcdfadf5351b3e054b94b8', pr_url=None, pr_revision=None, pr_num=None)"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.push_to_hub()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E0VmGDjEWlTu"
      },
      "source": [
        "Completed finetuning of the model!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O6o47-DBWgnQ"
      },
      "source": [
        "# Inference"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "G7ItAbaGWiAU",
        "outputId": "85cb7963-424d-4da6-cc2c-ccc0d60611ec"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/datasets/load.py:1491: FutureWarning: The repository for PolyAI/minds14 contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/PolyAI/minds14\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"
          ]
        }
      ],
      "source": [
        "from datasets import load_dataset, Audio\n",
        "\n",
        "dataset = load_dataset(\n",
        "    \"PolyAI/minds14\",\n",
        "    name = \"en-US\",\n",
        "    split = \"train\"\n",
        "    )\n",
        "dataset = dataset.cast_column(\"audio\", Audio(sampling_rate = 16000))\n",
        "\n",
        "sampling_rate = dataset.features[\"audio\"].sampling_rate\n",
        "audio_file = dataset[0][\"audio\"][\"path\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "wjUyhgmTX7pq",
        "outputId": "27f6c5d2-fa99-4661-ed91-6267b00a180d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[{'score': 0.08367979526519775, 'label': 'cash_deposit'},\n",
              " {'score': 0.07557357102632523, 'label': 'freeze'},\n",
              " {'score': 0.07528286427259445, 'label': 'app_error'},\n",
              " {'score': 0.07527675479650497, 'label': 'pay_bill'},\n",
              " {'score': 0.0749484971165657, 'label': 'direct_debit'}]"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "classifier = pipeline(\n",
        "    \"audio-classification\",\n",
        "    model = \"GSoC_wavLM_DetectionOfIntonationalUnits\",\n",
        ")\n",
        "\n",
        "classifier(audio_file)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "Vw5NlIzSYOdi",
        "outputId": "8a1e951e-d38e-4f81-8d3e-936ebabf39a7"
      },
      "outputs": [
        {
          "ename": "NameError",
          "evalue": "name 'torch' is not defined",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-31-1ad678fc7602>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m   \u001b[0mlogits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogits\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'torch' is not defined"
          ]
        }
      ],
      "source": [
        "with torch.no_grad():\n",
        "  logits = model(**inputs).logits"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "aGJtcUIqYom7"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "\n",
        "predicted_class_ids = torch.argmax(logits).item()\n",
        "predicted_label = model.config.id2label[predicted_class_ids]\n",
        "predicted_label"
      ]
    }
  ],
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