def main():
parser = _get_parser()
opt = parser.parse_args()
log_info(opt)
preprocess(opt.dataset_path, opt.preprocess_mode)
if opt.output_unit == 'character':
generate_character_labels(opt.dataset_path, opt.labels_dest)
generate_character_script(opt.dataset_path, opt.new_path,
opt.script_prefix, opt.labels_dest)
elif opt.output_unit == 'subword':
generate_sentencepiece_input(opt.dataset_path)
if not opt.use_pretrain_kobert_tokenizer:
train_sentencepiece(opt.dataset_path, opt.vocab_size)
generate_subword_labels('aihub_sentencepiece.vocab', opt.labels_dest,
opt.use_pretrain_kobert_tokenizer)
generate_subword_script(opt.dataset_path, opt.new_path,
opt.script_prefix)
elif opt.output_unit == 'grapheme':
character_to_grapheme(opt.dataset_path, opt.grapheme_save_path)
generate_grapheme_labels(opt.grapheme_save_path, opt.labels_dest)
generate_grapheme_script(opt.grapheme_save_path, opt.new_path,
opt.script_prefix, opt.labels_dest)
else:
raise ValueError("Unsupported preprocess method : {0}".format(
opt.output_unit))
gather_files(opt.dataset_path, opt.new_path)
def main():
parser = _get_parser()
opt = parser.parse_args()
log_info(opt)
audio_paths, transcripts = preprocess(opt.dataset_path, opt.preprocess_mode)
if opt.output_unit == 'character':
generate_character_labels(transcripts, opt.vocab_dest)
generate_character_script(audio_paths, transcripts, opt.vocab_dest)
elif opt.output_unit == 'subword':
train_sentencepiece(transcripts, opt.savepath, opt.vocab_size)
sentence_to_subwords(audio_paths, transcripts, opt.savepath)
elif opt.output_unit == 'grapheme':
sentence_to_grapheme(audio_paths, transcripts, opt.vocab_dest)
else:
raise ValueError("Unsupported preprocess method : {0}".format(opt.output_unit))