| 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() | |