Upload download_datasets_in_wav_or_mp3_and_create_csv.ipynb

download_datasets_in_wav_or_mp3_and_create_csv.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datasets import Dataset, load_dataset, DatasetDict, Audio, concatenate_datasets, load_from_disk, IterableDataset, interleave_datasets\n",
+    "import soundfile as sf, os, pandas as pd, re\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "max = 20.0\n",
+    "min = 1.0\n",
+    "\n",
+    "dataset = load_dataset(\"Sin2pi/JA_audio_JA_text_180k_samples\", split=\"train\", trust_remote_code=True, streaming=True)\n",
+    "\n",
+    "name = \"gvs\"\n",
+    "ouput_dir = \"./datasets/\"\n",
+    "output_file = 'metadata.csv'\n",
+    "os.makedirs(ouput_dir + name, exist_ok=True)\n",
+    "folder_path = ouput_dir + name # Create a folder to store the audio and transcription files\n",
+    "\n",
+    "char = '[　０１２３４５６７８９ａｂｃｄｅｆｇｈｉｊｋｌｍｎｏｐｑｒｓｔｕｖｗｘｙｚＡＢＣＤＥＦＧＨＩＪＫＬＭＮＯＰＱＲＳＴＵＶＷＸＹＺabcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ1234567890]'\n",
+    "special_characters = '[♬「」？！“%‘”~♪…?!゛＃＄％＆（）＊＋：；〈＝〉＠＾＿｛｜｝～\"█♩♫』『.;:<>_()*&^$#@`, ]'\n",
+    "\n",
+    "for i, sample in tqdm(enumerate(dataset)): # Process each sample in the filtered dataset\n",
+    "    audio_sample = name + f'_{i}.mp3' # or wav\n",
+    "    audio_path = os.path.join(folder_path, audio_sample)\n",
+    "    transcription_path = os.path.join(folder_path, output_file)  # Path to save transcription file \n",
+    "    sample[\"audio_length\"] = len(sample[\"audio\"][\"array\"]) / sample[\"audio\"][\"sampling_rate\"]  # Get audio length, remove if not needed\n",
+    "    sample[\"sentence\"] = re.sub(special_characters,'', sample[\"sentence\"])\n",
+    "    \n",
+    "    if not os.path.exists(audio_path) and bool(sample[\"sentence\"]) and sample[\"audio_length\"] > min and sample[\"audio_length\"] < max and not re.search(char, sample[\"sentence\"]) and sample[\"down_votes\"] == 0 and sample[\"up_votes\"] > 0:\n",
+    "        \n",
+    "        sf.write(audio_path, sample['audio']['array'], sample['audio']['sampling_rate'])\n",
+    "        with open(transcription_path, 'a', encoding='utf-8') as transcription_file: # Save transcription file\n",
+    "            transcription_file.write(audio_sample+\",\") # Save transcription file name  \n",
+    "            transcription_file.write(sample['sentence']) # Save transcription \n",
+    "            transcription_file.write('\\n') "
+   ]
+  }
+ ],
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+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}