Delete download_datasets_in_wav_or_mp3_and_create_csv.ipynb

download_datasets_in_wav_or_mp3_and_create_csv.ipynb

@@ -1,71 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "import soundfile as sf, os, re, neologdn\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' # create metadata file with file names and transcripts\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, out_file)  # Path to save transcription file \n",
-    "    if not os.path.exists(audio_path):\n",
-    "        patterns = [(r\"…\",'。'), (r\"!!\",'!'), (special_characters,\"\"), (r\"\\s+\", \"\")] # (r\"(.)\\1{2}\")\n",
-    "        for pattern, replace in patterns:\n",
-    "            sample[\"sentence\"] = re.sub(pattern, replace, sample[\"sentence\"])\n",
-    "        sample[\"sentence\"] = (neologdn.normalize(sample[\"sentence\"], repeat=1)) # for Japanese only, repeat number reduces repeat characters\n",
-    "        sample[\"sentence_length\"] = len(sample[\"sentence\"]) # Get sentence lengths  \n",
-    "        sample[\"audio_length\"] = len(sample[\"audio\"][\"array\"]) / sample[\"audio\"][\"sampling_rate\"]  # Get audio length, remove if not needed\n",
-    "        if bool(sample[\"sentence\"]) and max > sample[\"audio_length\"] > min and not re.search(char, sample[\"sentence\"]) and sample[\"sentence_length\"] > min_char:\n",
-    "            sf.write(audio_path, sample['audio']['array'], sample['audio']['sampling_rate']) # Get files      \n",
-    "            # process_directory(folder_path, (folder_path + \"/trimmed/\")) # for use with audio sample silence removal script\n",
-    "            if os.path.isfile(audio_path):\n",
-    "                os.remove(audio_path)\n",
-    "            with open(transcription_path, 'a', encoding='utf-8') as 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')"
-   ]
-  }
- ],
- "metadata": {
-  "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
-}