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import glob
from data_gen.tts.base_preprocess import BasePreprocessor
class WenetSpeechPreprocess(BasePreprocessor):
def meta_data(self):
wavfn2text = {}
def get_wavfn2text():
d = open(f'{self.raw_data_dir}/extracted_wav/wenetspeech.txt').readlines()
d = [l.strip().split("\t") for l in d if l.strip() != '' and 'podcast' in l]
d = {l[0]: l[1] for l in d}
wavfn2text.update(d)
get_wavfn2text()
all_wavs = sorted(wavfn2text.keys())
for wav_fn in all_wavs:
wav_basename = wav_fn.split("/")[-2]+"_"+wav_fn.split("/")[-1]
spk_name = 'asr_data'
item_name = f'{spk_name}_{wav_basename}'
yield {
'item_name': item_name,
'wav_fn': wav_fn.replace("/home/jzy/dict_idea/NeuralSeq/", ""),
'txt': wavfn2text[wav_fn],
'spk_name': spk_name
}
if __name__ == "__main__":
WenetSpeechPreprocess.process()
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