{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Prepare the dependencies" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "aMfkb196Yf6U" }, "outputs": [], "source": [ "#!pip install --upgrade datasets[audio]" ] }, { "cell_type": "markdown", "metadata": { "id": "V9eiWZHu8bsH" }, "source": [ "# Load libraries" ] }, { "cell_type": "code", "execution_count": 121, "metadata": { "id": "V452D53XYtjW" }, "outputs": [], "source": [ "from huggingface_hub import list_datasets\n", "from datasets import(load_dataset, DatasetDict, load_from_disk,\n", " Audio, IterableDatasetDict, Features, Value,\n", " Sequence, Dataset)\n", "import IPython.display as ipd\n", "from functools import partial" ] }, { "cell_type": "markdown", "metadata": { "id": "2sFdknNa8WN0" }, "source": [ "# Load Dataset" ] }, { "cell_type": "code", "execution_count": 122, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "GRuofoeIYt9k", "outputId": "00b7222b-76fa-42d9-cf5d-0d59ccb32633" }, "outputs": [ { "data": { "text/plain": [ "IterableDataset({\n", " features: ['Y', 'id', 'gender', 'filename'],\n", " n_shards: 1\n", "})" ] }, "execution_count": 122, "metadata": {}, "output_type": "execute_result" } ], "source": [ "voice_data = load_dataset(\"malaysia-ai/malay-conversational-speech-corpus\",\n", " streaming=True, split=\"train\", token=True)\n", "voice_data" ] }, { "cell_type": "code", "execution_count": 123, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "TJVngvaJBDTW", "outputId": "9ac82d8c-5f2d-4fb3-e375-d6d6e4a990ce" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "taken: 2592\n" ] }, { "data": { "text/plain": [ "IterableDatasetDict({\n", " train: IterableDataset({\n", " features: ['Y', 'id', 'gender', 'filename'],\n", " n_shards: 1\n", " })\n", " test: IterableDataset({\n", " features: ['Y', 'id', 'gender', 'filename'],\n", " n_shards: 1\n", " })\n", "})" ] }, "execution_count": 123, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def train_test_split(iterable_dataset, num_rows, split_ratio=0.8):\n", " taken = int(split_ratio * num_rows)\n", " print(f'taken: {taken}')\n", " raw_datasets = IterableDatasetDict()\n", " raw_datasets[\"train\"] = iterable_dataset.take(taken)\n", " raw_datasets[\"test\"] = iterable_dataset.skip(taken)\n", " return raw_datasets\n", "\n", "voice_data_split = train_test_split(voice_data, num_rows=3241, split_ratio=0.8)\n", "voice_data_split" ] }, { "cell_type": "code", "execution_count": 124, "metadata": { "id": "NPWTtiMdxzXQ" }, "outputs": [], "source": [ "def pre_processing(voice_data):\n", " voice_data = voice_data.filter(lambda x: x[\"id\"] != \"0\")\n", " voice_data = voice_data.rename_columns({\"Y\": \"sentence\", \"filename\": \"audio\"})\n", " voice_data = voice_data.remove_columns([\"id\", \"gender\"])\n", " voice_data = voice_data.cast(\n", " Features({'sentence': Value(dtype='string', id=None),\n", " 'audio': Audio(sampling_rate=16000, mono=True, decode=True, id=None)\n", " }))\n", " voice_data = voice_data.cast_column(\"audio\", Audio(sampling_rate=16000))\n", "\n", " return voice_data" ] }, { "cell_type": "code", "execution_count": 125, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "9OiIvOZNjCS_", "outputId": "fe19b3ef-0904-4e32-c6a2-27d9e07b7ca0" }, "outputs": [ { "data": { "text/plain": [ "DatasetDict({\n", " train: Dataset({\n", " features: ['sentence', 'audio'],\n", " num_rows: 2545\n", " })\n", " test: Dataset({\n", " features: ['sentence', 'audio'],\n", " num_rows: 604\n", " })\n", "})" ] }, "execution_count": 125, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def gen_from_iterable_dataset(iterable_ds):\n", " yield from iterable_ds\n", "\n", "def convert_to_dataset(callable_fn, dataset_split):\n", " row_data = DatasetDict()\n", " row_data[\"train\"] = Dataset.from_generator(\n", " partial(\n", " gen_from_iterable_dataset,\n", " callable_fn(dataset_split)[\"train\"]),\n", " features=callable_fn(dataset_split)[\"train\"].features)\n", " row_data[\"test\"] = Dataset.from_generator(\n", " partial(\n", " gen_from_iterable_dataset,\n", " callable_fn(dataset_split)[\"test\"]),\n", " features=callable_fn(dataset_split)[\"test\"].features)\n", " return row_data\n", "\n", "row_dataset = convert_to_dataset(pre_processing, voice_data_split)\n", "row_dataset" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 245, "referenced_widgets": [ "9cf67018ea6143cba5a7ac47848f3126", "6ddf101aac784f589b5e55e4a3afaf31", "fd4732efa3f345ba88820693924d66af", "cc1f129e8e6e470f8c908bfe0b52a120", "f8be3447ac9b478da7df3b482d94cabe", "7d03a04f0d3e40adb1f35dd9e2b867d8", "b7646c2b91b5437db4d5f579fef20155", "e62a1910d5d7423ea319b73543953e50", "e34fa3942f904199b2783de3e942ffcd", "7aba9da3c47b442e91b6bab4ccf7e2b9", "9491664cf4744452ad3f4825d5ef5304", "803b9b02a5dc40d99a61d9b52afe6aee", "b3133238bffe4083a8644e3a82139768", "da0ff529b6464b7c9a8dc2c2dc5e390e", "07dd5f8186624f1b990077f8e938f120", "f4e3ae2dcdf14046b473be7e1afb4d84", "3daa087d37f449d98d54979201af27a9", "cd30d3e04e86482b8063ffb8ebb8cd66", "22589d7ccfb34eb7802915fdaad5007b", "c507f3e1256647519acee85c5b0d9eed", "46ecc8146b0f42c297e2c9a92ecfe937", "5faaae0a3a724ec7b2d1c5671c158996", "af795e631743423ba6b04709aa476462", "00dd5dc3d3b34a7cabe43123cb048742", "fbdf939ed5524890921d1bf54e72ab3d", "9cb77165414d490d94dfa748bd3d3983", "eb9f31161e5a4932bc813389cac51623", "4303a90886034da89cac1281b96ab5bc", "bc2631482d60493c949b7b6bde70539e", "393984eb607b4fd8a61e08c586512265", "7fa57a4e742a41018a1d057a85b924d9", "0c05c415a4b7422e981f0eacc5bf9330", "afeec2b361e046ada2a58a792842647f", "746e69c702954ba59846996e3057b652", "fc4f514162c84dcb9c013b82b8adedb7", "92508af984eb4a2cafad9f89d939537e", "f946a8abac624afdae1bc969e54ec467", "d83b32ee23bf4d6fb2d2311a6ae7d56d", "272e7b334db84b1d8990013552657e3c", "4dbc796a11c144e1bfd84fe16a5f5fe4", "9cf97e13719046398223662accb90e83", "fb5ae0e0209e46faa82f79e694044dc6", "f9b43bf30b7845deb20077cb7651dd79", "ce8f8c1429d24c3e9979ea7758a52c5f", "3b3f579f906e44acb594c32045a3eb37", "f02ccb72a1fe46f6a719c032181ed8a6", "85427a8d28ca4ab9a6fbf4e363528e3d", "2d5df818602042f7b72ceb7512ffeb08", "8eb04b5004d742ef89cf3183e8ae9551", "adae2188716f40fb9ae38dccca222b93", "2e065d7a5c3f43df80e261e98613374a", "3f59f6ae7dca4af3994333f1acc9fb85", "fd44223b10c64e7c976a045263451762", "124dfba919eb4405b2b21b2a79f401ba", "dfc625302b4b4deda95fa485702b7d03", "c4af146267cd4a8b8001e466bfbd33ce", "0c0bfd2d5170408fb78cc74fd6733144", "696da88a4c734c35ad1985c00156fef9", "447141018692476b85d2cfc94e631f2b", "62943efba1dd41bb82c4d96c90c9fd5c", "62260b6c7d2041108873db6a0d7ffab7", "2b7396446769468f95df866bf2845565", "fb7b0d2b93754121a53d321251ea1cb0", "bddcabf30fda4dd582021238a76925fd", "49c787b1813c4e578c446dbe615a6832", "abed84d1c282427b9858faa20f11e9c2" ] }, "id": "0nGkqvhcjNfi", "outputId": "3f0101f1-8c73-4899-fe4f-c3c239ab1377" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9cf67018ea6143cba5a7ac47848f3126", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Uploading the dataset shards: 0%| | 0/1 [00:00