--- license: cc0-1.0 --- # 張悦楷三國演義 Fork from [laubonghaudoi/zoengjyutgaai_saamgwokjinji](https://huggingface.co/datasets/laubonghaudoi/zoengjyutgaai_saamgwokjinji) We found the original wav files are not splitted correctly, so we asked the author to provide the srt file and un-splitted wav files. We then re-split the wav files and align the srt file to the wav files. We also filtered some samples that are too short. ```python subtitles = [] splits = librosa.effects.split(audio) # shape: (682, 2) !mkdir -p dataset/zoengjyutgaai_saamgwokjinji/wavs # split audio by srt time for i, sub in enumerate(subs): chunk_start = sub.start.to_time() chunk_end = sub.end.to_time() chunk_start = ((chunk_start.minute * 60) + chunk_start.second) * sr chunk_end = ((chunk_end.minute * 60) + chunk_end.second) * sr # Find the closest split chunk_start = min(splits, key=lambda x: abs(x[0] - chunk_start))[0] chunk_end = min(splits, key=lambda x: abs(x[1] - chunk_end))[1] chunk = audio[chunk_start:chunk_end] wav_file = f"001_{i:03}.wav" # resample, since bert-vits2 training only support 44.1k try: chunk = librosa.resample(chunk, sr, 44100) except: print(f"Error resampling {wav_file}") continue subtitles.append({ 'path': wav_file, 'speaker': 'zoengjyutgaai', 'language': 'YUE', 'text': sub.text }) # export audio sf.write(f"dataset/zoengjyutgaai_saamgwokjinji/wavs/{wav_file}", chunk, 44100, subtype='PCM_16') df = pd.DataFrame(subtitles) df.to_csv("dataset/zoengjyutgaai_saamgwokjinji/001.csv", index=False, sep='|', header=False) ```