text: import argparse
  import os
  from multiprocessing import cpu_count
  
  #from datasets import preprocessor
  from datasets import preprocessor_viet_tts as preprocessor
  from hparams import hparams
  from tqdm import tqdm
  
  
  def preprocess(args, input_folders, out_dir, hparams):
  	mel_dir = os.path.join(out_dir, 'mels')
  	wav_dir = os.path.join(out_dir, 'audio')
  	linear_dir = os.path.join(out_dir, 'linear')
  	os.makedirs(mel_dir, exist_ok=True)
  	os.makedirs(wav_dir, exist_ok=True)
  	os.makedirs(linear_dir, exist_ok=True)
  	metadata = preprocessor.build_from_path(hparams, input_folders, mel_dir, linear_dir, wav_dir, args.n_jobs, tqdm=tqdm)
  	write_metadata(metadata, out_dir)
  
  def write_metadata(metadata, out_dir):
  	with open(os.path.join(out_dir, 'train.txt'), 'w', encoding='utf-8') as f:
  		for m in metadata:
  			f.write('|'.join([str(x) for x in m]) + '\n')
  	mel_frames = sum([int(m[4]) for m in metadata])
  	timesteps = sum([int(m[3]) for m in metadata])
  	sr = hparams.sample_rate
  	hours = timesteps / sr / 3600
  	print('Write {} utterances, {} mel frames, {} audio timesteps, ({:.2f} hours)'.format(
  		len(metadata), mel_frames, timesteps, hours))
  	print('Max input length (text chars): {}'.format(max(len(m[5]) for m in metadata)))
  	print('Max mel frames length: {}'.format(max(int(m[4]) for m in metadata)))
  	print('Max audio timesteps length: {}'.format(max(m[3] for m in metadata)))
  
  def norm_data(args):
  
  	merge_books = (args.merge_books=='True')
  
  	print('Selecting data folders..')
  	supported_datasets = ['LJSpeech-1.0', 'LJSpeech-1.1', 'M-AILABS', 'Viet-TTS']
  	if args.dataset not in supported_datasets:
  		raise ValueError('dataset value entered {} does not belong to supported datasets: {}'.format(
  			args.dataset, supported_datasets))
  
  	if args.dataset.startswith('LJSpeech'):
  		return [os.path.join(args.base_dir, args.dataset)]
  
  
  	if args.dataset == 'M-AILABS':
  		supported_languages = ['en_US', 'en_UK', 'fr_FR', 'it_IT', 'de_DE', 'es_ES', 'ru_RU',
  			'uk_UK', 'pl_PL', 'nl_NL', 'pt_PT', 'fi_FI', 'se_SE', 'tr_TR', 'ar_SA']
  		if args.language not in supported_languages:
  			raise ValueError('Please enter a supported language to use from M-AILABS dataset! \n{}'.format(
  				supported_languages))
  
  		supported_voices = ['female', 'male', 'mix']
  		if args.voice not in supported_voices:
  			raise ValueError('Please enter a supported voice option to use from M-AILABS dataset! \n{}'.format(
  				supported_voices))
  
  		path = os.path.join(args.base_dir, args.language, 'by_book', args.voice)
  		supported_readers = [e for e in os.listdir(path) if os.path.isdir(os.path.join(path,e))]
  		if args.reader not in supported_readers:
  			raise ValueError('Please enter a valid reader for your language and voice settings! \n{}'.format(
  				supported_readers))
  
  		path = os.path.join(path, args.reader)
  		supported_books = [e for e in os.listdir(path) if os.path.isdir(os.path.join(path,e))]
  		if merge_books:
  			return [os.path.join(path, book) for book in supported_books]
  
  		else:
  			if args.book not in supported_books:
  				raise ValueError('Please enter a valid book for your reader settings! \n{}'.format(
  					supported_books))
  
  			return [os.path.join(path, args.book)]
  
  	if args.dataset == "Viet-TTS":
  		return [os.path.join(args.base_dir, args.dataset)]
  
  
  def run_preprocess(args, hparams):
  	input_folders = norm_data(args)
  	output_folder = os.path.join(args.base_dir, args.output)
  
  	preprocess(args, input_folders, output_folder, hparams)
  
  
  def main():
  	print('initializing preprocessing..')
  	parser = argparse.ArgumentParser()
  	parser.add_argument('--base_dir', default='')
  	parser.add_argument('--hparams', default='',
  		help='Hyperparameter overrides as a comma-separated list of name=value pairs')
  	parser.add_argument('--dataset', default='Viet-TTS')
  	parser.add_argument('--language', default='en_US')
  	parser.add_argument('--voice', default='female')
  	parser.add_argument('--reader', default='mary_ann')
  	parser.add_argument('--merge_books', default='False')
  	parser.add_argument('--book', default='northandsouth')
  	parser.add_argument('--output', default='training_data')
  	parser.add_argument('--n_jobs', type=int, default=cpu_count())
  	args = parser.parse_args()
  
  	modified_hp = hparams.parse(args.hparams)
  
  	assert args.merge_books in ('False', 'True')
  
  	run_preprocess(args, modified_hp)
  
  
  if __name__ == '__main__':
  	main()
  