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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "347ace04",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"# VCTK Corpus Path\n",
"__CORPUSPATH__ = os.path.expanduser(\"~/data/VCTK-Corpus\") \n",
"\n",
"# output path\n",
"__OUTPATH__ = \"./Data\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4ce9eb2e",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from scipy.io import wavfile\n",
"from pydub import AudioSegment\n",
"\n",
"from pydub import AudioSegment\n",
"from pydub.silence import split_on_silence\n",
"import os\n",
"\n",
"def split(sound):\n",
" dBFS = sound.dBFS\n",
" chunks = split_on_silence(sound,\n",
" min_silence_len = 100,\n",
" silence_thresh = dBFS-16,\n",
" keep_silence = 100\n",
" )\n",
" return chunks\n",
"\n",
"def combine(_src):\n",
" audio = AudioSegment.empty()\n",
" for i,filename in enumerate(os.listdir(_src)):\n",
" if filename.endswith('.wav'):\n",
" filename = os.path.join(_src, filename)\n",
" audio += AudioSegment.from_wav(filename)\n",
" return audio\n",
"\n",
"def save_chunks(chunks, directory):\n",
" if not os.path.exists(directory):\n",
" os.makedirs(directory)\n",
" counter = 0\n",
"\n",
" target_length = 5 * 1000\n",
" output_chunks = [chunks[0]]\n",
" for chunk in chunks[1:]:\n",
" if len(output_chunks[-1]) < target_length:\n",
" output_chunks[-1] += chunk\n",
" else:\n",
" # if the last output chunk is longer than the target length,\n",
" # we can start a new one\n",
" output_chunks.append(chunk)\n",
"\n",
" for chunk in output_chunks:\n",
" chunk = chunk.set_frame_rate(24000)\n",
" chunk = chunk.set_channels(1)\n",
" counter = counter + 1\n",
" chunk.export(os.path.join(directory, str(counter) + '.wav'), format=\"wav\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "769a7f62",
"metadata": {},
"outputs": [],
"source": [
"# Source: http://speech.ee.ntu.edu.tw/~jjery2243542/resource/model/is18/en_speaker_used.txt\n",
"# Source: https://github.com/jjery2243542/voice_conversion\n",
"\n",
"speakers = [225,228,229,230,231,233,236,239,240,244,226,227,232,243,254,256,258,259,270,273]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9302fb6a",
"metadata": {},
"outputs": [],
"source": [
"# downsample to 24 kHz\n",
"\n",
"for p in speakers:\n",
" directory = __OUTPATH__ + '/p' + str(p)\n",
" if not os.path.exists(directory):\n",
" audio = combine(__CORPUSPATH__ + '/wav48/p' + str(p))\n",
" chunks = split(audio)\n",
" save_chunks(chunks, directory)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4b0ca022",
"metadata": {},
"outputs": [],
"source": [
"# get all speakers\n",
"\n",
"data_list = []\n",
"for path, subdirs, files in os.walk(__OUTPATH__):\n",
" for name in files:\n",
" if name.endswith(\".wav\"):\n",
" speaker = int(path.split('/')[-1].replace('p', ''))\n",
" if speaker in speakers:\n",
" data_list.append({\"Path\": os.path.join(path, name), \"Speaker\": int(speakers.index(speaker)) + 1})\n",
" \n",
"import pandas as pd\n",
"\n",
"data_list = pd.DataFrame(data_list)\n",
"data_list = data_list.sample(frac=1)\n",
"\n",
"import random\n",
"\n",
"split_idx = round(len(data_list) * 0.1)\n",
"\n",
"test_data = data_list[:split_idx]\n",
"train_data = data_list[split_idx:]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "88df2a45",
"metadata": {},
"outputs": [],
"source": [
"# write to file \n",
"\n",
"file_str = \"\"\n",
"for index, k in train_data.iterrows():\n",
" file_str += k['Path'] + \"|\" +str(k['Speaker'] - 1)+ '\\n'\n",
"text_file = open(__OUTPATH__ + \"/train_list.txt\", \"w\")\n",
"text_file.write(file_str)\n",
"text_file.close()\n",
"\n",
"file_str = \"\"\n",
"for index, k in test_data.iterrows():\n",
" file_str += k['Path'] + \"|\" + str(k['Speaker'] - 1) + '\\n'\n",
"text_file = open(__OUTPATH__ + \"/val_list.txt\", \"w\")\n",
"text_file.write(file_str)\n",
"text_file.close()"
]
}
],
"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": "3.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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