01-Data preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "75b58048-7d14-4fc6-8085-1fc08c81b4a6"
   },
   "source": [
    "# 01 - Data preprocessing and feature engineering"
   ],
   "id": "75b58048-7d14-4fc6-8085-1fc08c81b4a6"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "55fb8d21-df06-472a-99dd-b59567be6dad"
   },
   "source": [
    "## Prepare Environment"
   ],
   "id": "55fb8d21-df06-472a-99dd-b59567be6dad"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "e68ea9f8-9b61-414e-8885-3033b67c2850",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1701989279938,
     "user_tz": -60,
     "elapsed": 52317,
     "user": {
      "displayName": "Federico Bono",
      "userId": "14903631917519687760"
     }
    },
    "outputId": "c5a687db-ba49-46f2-cf89-88d7173a20f4"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Collecting git+https://github.com/huggingface/transformers\n",
      "  Cloning https://github.com/huggingface/transformers to /tmp/pip-req-build-yhkqi_jn\n",
      "  Running command git clone --filter=blob:none --quiet https://github.com/huggingface/transformers /tmp/pip-req-build-yhkqi_jn\n",
      "  Resolved https://github.com/huggingface/transformers to commit 0ea42ef0f9f71deba7775ead33afa0e493823d60\n",
      "  Installing build dependencies ... \u001B[?25l\u001B[?25hdone\n",
      "  Getting requirements to build wheel ... \u001B[?25l\u001B[?25hdone\n",
      "  Preparing metadata (pyproject.toml) ... \u001B[?25l\u001B[?25hdone\n",
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     ]
    }
   ],
   "source": [
    "!pip install accelerate>=0.20.1\n",
    "!pip install datasets>=2.6.1\n",
    "!pip install git+https://github.com/huggingface/transformers\n",
    "!pip install librosa"
   ],
   "id": "e68ea9f8-9b61-414e-8885-3033b67c2850"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Huggingface login\n",
    "We need it to download the dataset"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "127c3784f008363d"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 145,
     "referenced_widgets": [
      "89371cdda23c4f2ab7e7110e6ede583a",
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      "e3b7655d58e6416ab2991e2e9cb8b5b2",
      "0e086d3c8d3e4a9c91e286dbdd9c1105",
      "044fd5b17bf248d1abccc0e4ef59afec",
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      "7d193a9978464885b21628f479277f13",
      "6e95da2ee4c2439d96430ddd3ee89827",
      "cdc03b1f59d24b769c879cc76e343f73",
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      "bca6276331544243b76c50f70eb0b2be",
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      "094ee851d76d459ca1b6d4c7e57ad6a6",
      "7cd59526897345039ed1a647b6b462f9",
      "52425b49a3eb4a9f85433add1daa1972",
      "07384f5a1c7543c3b9c1567ea8ff4a1a"
     ]
    },
    "executionInfo": {
     "elapsed": 466,
     "status": "ok",
     "timestamp": 1701989280387,
     "user": {
      "displayName": "Federico Bono",
      "userId": "14903631917519687760"
     },
     "user_tz": -60
    },
    "id": "b045a39e-2a3e-4153-bdb5-281500bcd348",
    "outputId": "3e7fc037-e57f-4dc7-9a5a-2480f7f50661"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "89371cdda23c4f2ab7e7110e6ede583a"
      }
     },
     "metadata": {}
    }
   ],
   "source": [
    "from huggingface_hub import notebook_login\n",
    "\n",
    "notebook_login()"
   ],
   "id": "b045a39e-2a3e-4153-bdb5-281500bcd348"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "844a4861-929c-4762-b29b-80b1e95aba4b"
   },
   "source": [
    "We will use Google Drive as a feature store"
   ],
   "id": "844a4861-929c-4762-b29b-80b1e95aba4b"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "JDlJmf9T-C6c",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1701989301328,
     "user_tz": -60,
     "elapsed": 14371,
     "user": {
      "displayName": "Federico Bono",
      "userId": "14903631917519687760"
     }
    },
    "outputId": "77f54ee2-5b86-4de2-fd34-487ed32ecc25"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Mounted at /gdrive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/gdrive')\n",
    "\n",
    "DATASET_RAW = '/gdrive/My Drive/scalable/dataset_raw/'\n",
    "DATASET = '/gdrive/My Drive/scalable/dataset/'\n",
    "DATASET_MAP = '/gdrive/My Drive/scalable/dataset_mapped/'"
   ],
   "id": "JDlJmf9T-C6c"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1d85d613-1c7e-46ac-9134-660bbe7ebc9d"
   },
   "source": [
    "We'll employ several popular Python packages to fine-tune the Whisper model.\n",
    "We'll use `datasets` to download and prepare our training data and\n",
    "`transformers` to load and train our Whisper model. We'll also require\n",
    "the `soundfile` package to pre-process audio files."
   ],
   "id": "1d85d613-1c7e-46ac-9134-660bbe7ebc9d"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b219c9dd-39b6-4a95-b2a1-3f547a1e7bc0"
   },
   "source": [
    "## Load Dataset"
   ],
   "id": "b219c9dd-39b6-4a95-b2a1-3f547a1e7bc0"
  },
  {
   "cell_type": "code",
   "execution_count": null,
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     "base_uri": "https://localhost:8080/",
     "height": 1000,
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      "8b9b9e65afcc49a4a1305ae8df151ed4",
      "ce225061064e41238ff9b2c8527fc98f",
      "8579df6221544bdb94c56b957f53ce52"
     ]
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1701951606391,
     "user_tz": -60,
     "elapsed": 113465,
     "user": {
      "displayName": "Federico Bono",
      "userId": "14903631917519687760"
     }
    },
    "outputId": "8d3cc264-6c63-4607-bdc9-964bbec151ee"
   },
   "outputs": [],
   "source": [
    "from datasets import load_dataset, DatasetDict\n",
    "from datasets import load_from_disk\n",
    "import os\n",
    "\n",
    "common_voice = DatasetDict()\n",
    "\n",
    "if not os.path.exists(DATASET_RAW):\n",
    "  common_voice[\"train_0\"] = load_dataset(\"mozilla-foundation/common_voice_11_0\", \"it\", split=\"train[:2500]\", use_auth_token=True, keep_in_memory=True)\n",
    "  common_voice[\"train_1\"] = load_dataset(\"mozilla-foundation/common_voice_11_0\", \"it\", split=\"train[2500:5000]\", use_auth_token=True, keep_in_memory=True)\n",
    "  common_voice[\"train_2\"] = load_dataset(\"mozilla-foundation/common_voice_11_0\", \"it\", split=\"train[5000:7500]\", use_auth_token=True, keep_in_memory=True)\n",
    "  common_voice[\"train_3\"] = load_dataset(\"mozilla-foundation/common_voice_11_0\", \"it\", split=\"train[7500:10000]\", use_auth_token=True, keep_in_memory=True)\n",
    "  common_voice[\"test\"] = load_dataset(\"mozilla-foundation/common_voice_11_0\", \"it\", split=\"test[:2500]\", use_auth_token=True, keep_in_memory=True)\n",
    "\n",
    "  os.mkdir(DATASET_RAW)\n",
    "  print(common_voice)\n",
    "\n",
    "  common_voice.save_to_disk(DATASET_RAW)\n",
    "\n",
    "  print('Dataset saved!')\n",
    "\n",
    "else:\n",
    "  common_voice = load_from_disk(DATASET_RAW, keep_in_memory=False)\n"
   ],
   "id": "a2787582-554f-44ce-9f38-4180a5ed6b44"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d5c7c3d6-7197-41e7-a088-49b753c1681f"
   },
   "source": [
    "Most ASR datasets only provide input audio samples (`audio`) and the\n",
    "corresponding transcribed text (`sentence`). Common Voice contains additional\n",
    "metadata information, such as `accent` and `locale`, which we can disregard for ASR.\n",
    "Keeping the notebook as general as possible, we only consider the input audio and\n",
    "transcribed text for fine-tuning, discarding the additional metadata information:"
   ],
   "id": "d5c7c3d6-7197-41e7-a088-49b753c1681f"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "20ba635d-518c-47ac-97ee-3cad25f1e0ce",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1701903201036,
     "user_tz": -60,
     "elapsed": 206,
     "user": {
      "displayName": "Federico Bono",
      "userId": "14903631917519687760"
     }
    },
    "outputId": "8bec4d9d-f916-490b-f30c-5b1d97e64bc0"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "DatasetDict({\n",
      "    train_0: Dataset({\n",
      "        features: ['audio', 'sentence'],\n",
      "        num_rows: 2500\n",
      "    })\n",
      "    train_1: Dataset({\n",
      "        features: ['audio', 'sentence'],\n",
      "        num_rows: 2500\n",
      "    })\n",
      "    train_2: Dataset({\n",
      "        features: ['audio', 'sentence'],\n",
      "        num_rows: 2500\n",
      "    })\n",
      "    train_3: Dataset({\n",
      "        features: ['audio', 'sentence'],\n",
      "        num_rows: 2500\n",
      "    })\n",
      "    test: Dataset({\n",
      "        features: ['audio', 'sentence'],\n",
      "        num_rows: 2500\n",
      "    })\n",
      "})\n"
     ]
    }
   ],
   "source": [
    "common_voice = common_voice.remove_columns([\"accent\", \"age\", \"client_id\", \"down_votes\", \"gender\", \"locale\", \"path\", \"segment\", \"up_votes\"])\n",
    "\n",
    "print(common_voice)"
   ],
   "id": "20ba635d-518c-47ac-97ee-3cad25f1e0ce"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2d63b2d2-f68a-4d74-b7f1-5127f6d16605"
   },
   "source": [
    "## Prepare Feature Extractor, Tokenizer and Data"
   ],
   "id": "2d63b2d2-f68a-4d74-b7f1-5127f6d16605"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "601c3099-1026-439e-93e2-5635b3ba5a73"
   },
   "source": [
    "The ASR pipeline can be de-composed into three stages:\n",
    "1) A feature extractor which pre-processes the raw audio-inputs\n",
    "2) The model which performs the sequence-to-sequence mapping\n",
    "3) A tokenizer which post-processes the model outputs to text format\n",
    "\n",
    "In 🤗 Transformers, the Whisper model has an associated feature extractor and tokenizer,\n",
    "called [WhisperFeatureExtractor](https://huggingface.co/docs/transformers/main/model_doc/whisper#transformers.WhisperFeatureExtractor)\n",
    "and [WhisperTokenizer](https://huggingface.co/docs/transformers/main/model_doc/whisper#transformers.WhisperTokenizer)\n",
    "respectively.\n",
    "\n",
    "We'll go through details for setting-up the feature extractor and tokenizer one-by-one!"
   ],
   "id": "601c3099-1026-439e-93e2-5635b3ba5a73"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "560332eb-3558-41a1-b500-e83a9f695f84"
   },
   "source": [
    "### Load WhisperFeatureExtractor"
   ],
   "id": "560332eb-3558-41a1-b500-e83a9f695f84"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "32ec8068-0bd7-412d-b662-0edb9d1e7365"
   },
   "source": [
    "The Whisper feature extractor performs two operations:\n",
    "1. Pads / truncates the audio inputs to 30s: any audio inputs shorter than 30s are padded to 30s with silence (zeros), and those longer that 30s are truncated to 30s\n",
    "2. Converts the audio inputs to _log-Mel spectrogram_ input features, a visual representation of the audio and the form of the input expected by the Whisper model"
   ],
   "id": "32ec8068-0bd7-412d-b662-0edb9d1e7365"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b2ef54d5-b946-4c1d-9fdc-adc5d01b46aa"
   },
   "source": [
    "We'll load the feature extractor from the pre-trained checkpoint with the default values:"
   ],
   "id": "b2ef54d5-b946-4c1d-9fdc-adc5d01b46aa"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "bc77d7bb-f9e2-47f5-b663-30f7a4321ce5",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
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      "e07bfdfa09ea4ca2b8a585516795eb27",
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      "273e3ad9f927480585d80013eeb36956",
      "3d9a23da5b9e4c009adb39a529d61681",
      "4f06dcdba66c477ead72dbc5ef74e72c"
     ]
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1701989316872,
     "user_tz": -60,
     "elapsed": 5129,
     "user": {
      "displayName": "Federico Bono",
      "userId": "14903631917519687760"
     }
    },
    "outputId": "81987be9-f4d7-438c-8fcc-b24f45ff8287"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "preprocessor_config.json:   0%|          | 0.00/185k [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "db24782e601740de9c763d1932fec6cd"
      }
     },
     "metadata": {}
    }
   ],
   "source": [
    "from transformers import WhisperFeatureExtractor\n",
    "\n",
    "feature_extractor = WhisperFeatureExtractor.from_pretrained(\"openai/whisper-small\")"
   ],
   "id": "bc77d7bb-f9e2-47f5-b663-30f7a4321ce5"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "93748af7-b917-4ecf-a0c8-7d89077ff9cb"
   },
   "source": [
    "### Load WhisperTokenizer"
   ],
   "id": "93748af7-b917-4ecf-a0c8-7d89077ff9cb"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2bc82609-a9fb-447a-a2af-99597c864029"
   },
   "source": [
    "The Whisper model outputs a sequence of _token ids_. The tokenizer maps each of these token ids to their corresponding text string. For Hindi, we can load the pre-trained tokenizer and use it for fine-tuning without any further modifications. We simply have to\n",
    "specify the target language and the task. These arguments inform the\n",
    "tokenizer to prefix the language and task tokens to the start of encoded\n",
    "label sequences:"
   ],
   "id": "2bc82609-a9fb-447a-a2af-99597c864029"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "c7b07f9b-ae0e-4f89-98f0-0c50d432eab6",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 241,
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    "executionInfo": {
     "status": "ok",
     "timestamp": 1701989318202,
     "user_tz": -60,
     "elapsed": 1331,
     "user": {
      "displayName": "Federico Bono",
      "userId": "14903631917519687760"
     }
    },
    "outputId": "039cbebe-7cbf-43a1-fdc2-7472a7988cbe"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "tokenizer_config.json:   0%|          | 0.00/805 [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "286fe79bf0594228a3bf060c6b8478ef"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "vocab.json:   0%|          | 0.00/836k [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "ace5faa967754d9084815fbf28136ac2"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "tokenizer.json:   0%|          | 0.00/2.48M [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "344aa654b4444a1b8d3c71bac6a963cf"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "merges.txt:   0%|          | 0.00/494k [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "0124605bc1394e089b14c685c195e34a"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "normalizer.json:   0%|          | 0.00/52.7k [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "bf54615ea75b47a88810217f1d47b6d1"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "added_tokens.json:   0%|          | 0.00/34.6k [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "cc7e2b63dc694bbc8113bfa69c8a8c15"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "special_tokens_map.json:   0%|          | 0.00/2.08k [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "52c2a76195634d37b3c05152c1261053"
      }
     },
     "metadata": {}
    }
   ],
   "source": [
    "from transformers import WhisperTokenizer\n",
    "\n",
    "tokenizer = WhisperTokenizer.from_pretrained(\"openai/whisper-small\", language=\"Italian\", task=\"transcribe\")"
   ],
   "id": "c7b07f9b-ae0e-4f89-98f0-0c50d432eab6"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d2ef23f3-f4a8-483a-a2dc-080a7496cb1b"
   },
   "source": [
    "### Combine To Create A WhisperProcessor"
   ],
   "id": "d2ef23f3-f4a8-483a-a2dc-080a7496cb1b"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5ff67654-5a29-4bb8-a69d-0228946c6f8d"
   },
   "source": [
    "To simplify using the feature extractor and tokenizer, we can _wrap_\n",
    "both into a single `WhisperProcessor` class. This processor object\n",
    "inherits from the `WhisperFeatureExtractor` and `WhisperProcessor`,\n",
    "and can be used on the audio inputs and model predictions as required.\n",
    "In doing so, we only need to keep track of two objects during training:\n",
    "the `processor` and the `model`:"
   ],
   "id": "5ff67654-5a29-4bb8-a69d-0228946c6f8d"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "77d9f0c5-8607-4642-a8ac-c3ab2e223ea6"
   },
   "outputs": [],
   "source": [
    "from transformers import WhisperProcessor\n",
    "\n",
    "processor = WhisperProcessor.from_pretrained(\"openai/whisper-small\", language=\"Italian\", task=\"transcribe\")"
   ],
   "id": "77d9f0c5-8607-4642-a8ac-c3ab2e223ea6"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "381acd09-0b0f-4d04-9eb3-f028ac0e5f2c"
   },
   "source": [
    "### Prepare Data"
   ],
   "id": "381acd09-0b0f-4d04-9eb3-f028ac0e5f2c"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5a679f05-063d-41b3-9b58-4fc9c6ccf4fd"
   },
   "source": [
    "Since\n",
    "our input audio is sampled at 48kHz, we need to _downsample_ it to\n",
    "16kHz prior to passing it to the Whisper feature extractor, 16kHz being the sampling rate expected by the Whisper model.\n",
    "\n",
    "We'll set the audio inputs to the correct sampling rate using dataset's\n",
    "[`cast_column`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=cast_column#datasets.DatasetDict.cast_column)\n",
    "method. This operation does not change the audio in-place,\n",
    "but rather signals to `datasets` to resample audio samples _on the fly_ the\n",
    "first time that they are loaded:"
   ],
   "id": "5a679f05-063d-41b3-9b58-4fc9c6ccf4fd"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "f12e2e57-156f-417b-8cfb-69221cc198e8"
   },
   "outputs": [],
   "source": [
    "from datasets import Audio\n",
    "\n",
    "common_voice = common_voice.cast_column(\"audio\", Audio(sampling_rate=16000))"
   ],
   "id": "f12e2e57-156f-417b-8cfb-69221cc198e8"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "91edc72d-08f8-4f01-899d-74e65ce441fc"
   },
   "source": [
    "Now we can write a function to prepare our data ready for the model:\n",
    "1. We load and resample the audio data by calling `batch[\"audio\"]`. As explained above, 🤗 Datasets performs any necessary resampling operations on the fly.\n",
    "2. We use the feature extractor to compute the log-Mel spectrogram input features from our 1-dimensional audio array.\n",
    "3. We encode the transcriptions to label ids through the use of the tokenizer."
   ],
   "id": "91edc72d-08f8-4f01-899d-74e65ce441fc"
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "6525c478-8962-4394-a1c4-103c54cce170",
    "ExecuteTime": {
     "end_time": "2023-12-07T23:04:08.213627Z",
     "start_time": "2023-12-07T23:04:08.203064Z"
    }
   },
   "outputs": [],
   "source": [
    "def prepare_dataset(batch):\n",
    "    # load and resample audio data from 48 to 16kHz\n",
    "    audio = batch[\"audio\"]\n",
    "\n",
    "    # compute log-Mel input features from input audio array\n",
    "    batch[\"input_features\"] = feature_extractor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_features[0]\n",
    "\n",
    "    # encode target text to label ids\n",
    "    batch[\"labels\"] = tokenizer(batch[\"sentence\"]).input_ids\n",
    "    return batch"
   ],
   "id": "6525c478-8962-4394-a1c4-103c54cce170"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "70b319fb-2439-4ef6-a70d-a47bf41c4a13"
   },
   "source": [
    "We can apply the data preparation function to all of our training examples using dataset's `.map` method. The argument `num_proc` specifies how many CPU cores to use. Setting `num_proc` > 1 will enable multiprocessing. If the `.map` method hangs with multiprocessing, set `num_proc=1` and process the dataset sequentially."
   ],
   "id": "70b319fb-2439-4ef6-a70d-a47bf41c4a13"
  },
  {
   "cell_type": "code",
   "source": [
    "from datasets import load_dataset, DatasetDict\n",
    "from datasets import load_from_disk\n",
    "import os\n",
    "\n",
    "if not os.path.exists(DATASET_MAP):\n",
    "  common_voice['train_0'] = common_voice['train_0'].map(prepare_dataset, num_proc=1, keep_in_memory=False)\n",
    "  common_voice['train_1'] = common_voice['train_1'].map(prepare_dataset, num_proc=1)\n",
    "  common_voice['train_2'] = common_voice['train_2'].map(prepare_dataset, num_proc=1)\n",
    "  common_voice['train_3'] = common_voice['train_3'].map(prepare_dataset, num_proc=1)\n",
    "  common_voice['test'] = common_voice['test'].map(prepare_dataset, num_proc=1)\n",
    "\n",
    "  os.mkdir(DATASET_MAP)\n",
    "  common_voice.save_to_disk(DATASET_MAP)\n",
    "else:\n",
    "  common_voice = load_from_disk(DATASET_MAP, keep_in_memory=False)\n"
   ],
   "metadata": {
    "id": "JDMABZXmvUgs"
   },
   "id": "JDMABZXmvUgs",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "from datasets import concatenate_datasets\n",
    "train_dataset = concatenate_datasets([common_voice['train_0'], common_voice['train_1'], common_voice['train_2'], common_voice['train_3']])"
   ],
   "metadata": {
    "id": "uRlp0BNVaonb"
   },
   "id": "uRlp0BNVaonb",
   "execution_count": null,
   "outputs": []
  },
  {
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    "train_dataset"
   ],
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    },
    "executionInfo": {
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     "timestamp": 1701989366301,
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     "elapsed": 3,
     "user": {
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      "userId": "14903631917519687760"
     }
    },
    "outputId": "de8b6e48-c788-48d7-f57e-c09b08412497"
   },
   "id": "PyXzt1oX2v2A",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['audio', 'sentence', 'input_features', 'labels'],\n",
       "    num_rows: 10000\n",
       "})"
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ]
  }
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