Upload malay_speech_3k_preparation.ipynb

Conversion stream iterable dataset to dataset and train test split based on ratio.

malay_speech_3k_preparation.ipynb

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+{
+  "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,
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