mirror of
https://github.com/thorstenMueller/Thorsten-Voice.git
synced 2024-11-25 01:13:10 +01:00
120 lines
4.0 KiB
Python
120 lines
4.0 KiB
Python
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# This script generates the folder structure for ljspeech-1.1 processing from mimic-recording-studio database
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# Changelog
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# v1.0 - Initial release by Thorsten Müller (https://github.com/thorstenMueller/deep-learning-german-tts)
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# v1.1 - Great improvements by Peter Schmalfeldt (https://github.com/manifestinteractive)
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# - Audio processing with ffmpeg (mono and samplerate of 22.050 Hz)
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# - Much better Python coding than my original version
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# - Greater logging output to command line
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# - See more details here: https://gist.github.com/manifestinteractive/6fd9be62d0ede934d4e1171e5e751aba
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# - Thanks Peter, it's a great contribution :-)
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# v1.2 - Added choice for choosing which recording session should be exported as LJSpeech
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import glob
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import sqlite3
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import ffmpeg
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import os
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from shutil import copyfile
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from shutil import rmtree
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# Setup Directory Data
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cwd = os.path.dirname(os.path.abspath(__file__))
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mrs_dir = os.path.join(cwd, os.pardir, "mimic-recording-studio")
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output_dir = os.path.join(cwd, "dataset")
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output_dir_audio = ""
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output_dir_audio_temp=""
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output_dir_speech = ""
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# Create folders needed for ljspeech
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def create_folders():
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global output_dir
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global output_dir_audio
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global output_dir_audio_temp
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global output_dir_speech
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print('→ Creating Dataset Folders')
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output_dir_speech = os.path.join(output_dir, "LJSpeech-1.1")
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# Delete existing folder if exists for clean run
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if os.path.exists(output_dir_speech):
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rmtree(output_dir_speech)
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output_dir_audio = os.path.join(output_dir_speech, "wavs")
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output_dir_audio_temp = os.path.join(output_dir_speech, "temp")
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# Create Clean Folders
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os.makedirs(output_dir_speech)
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os.makedirs(output_dir_audio)
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os.makedirs(output_dir_audio_temp)
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def convert_audio():
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global output_dir_audio
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global output_dir_audio_temp
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recordings = len([name for name in os.listdir(output_dir_audio_temp) if os.path.isfile(os.path.join(output_dir_audio_temp,name))])
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print('→ Converting %s Audio Files to 22050 Hz, 16 Bit, Mono\n' % "{:,}".format(recordings))
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for idx, wav in enumerate(glob.glob(os.path.join(output_dir_audio_temp, "*.wav"))):
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percent = (idx + 1) / recordings
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print('› \033[96m%s\033[0m \033[2m%s / %s (%s)\033[0m ' % (os.path.basename(wav), "{:,}".format((idx + 1)), "{:,}".format(recordings), "{:.0%}".format(percent)))
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# Convert WAV file to required format
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(ffmpeg
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.input(wav)
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.output(os.path.join(output_dir_audio, os.path.basename(wav)), acodec='pcm_s16le', ac=1, ar=22050, loglevel='error')
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.overwrite_output()
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.run(capture_stdout=True)
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)
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# Delete Temp File
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os.remove(wav)
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# Remove Temp Folder
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rmtree(output_dir_audio_temp)
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def create_meta_data():
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print('→ Creating META Data')
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conn = sqlite3.connect(os.path.join(mrs_dir, "backend", "db", "mimicstudio.db"))
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c = conn.cursor()
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# Create metadata.csv for ljspeech
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metadata = open(os.path.join(output_dir_speech, "metadata.csv"), mode="w", encoding="utf8")
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# List available recording sessions
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user_models = c.execute('SELECT uuid, user_name from usermodel ORDER BY created_date DESC').fetchall()
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user_id = user_models[0][0]
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for row in user_models:
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print(row[0] + ' -> ' + row[1])
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user_answer = input('Please choose ID of recording session to export (default is newest session) [' + user_id + ']: ')
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if user_answer:
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user_id = user_answer
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for row in c.execute('SELECT audio_id, prompt, lower(prompt) FROM audiomodel WHERE user_id = "' + user_id + '" ORDER BY length(prompt)'):
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metadata.write(row[0] + "|" + row[1] + "|" + row[2] + "\n")
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copyfile(os.path.join(mrs_dir, "backend", "audio_files", user_id, row[0] + ".wav"), os.path.join(output_dir_audio_temp, row[0] + ".wav"))
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metadata.close()
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conn.close()
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def main():
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print('\n\033[48;5;22m MRS to LJ Speech Processor \033[0m\n')
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create_folders()
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create_meta_data()
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convert_audio()
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print('\n\033[38;5;86;1m✔\033[0m COMPLETE【ツ】\n')
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if __name__ == '__main__':
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main()
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