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--- |
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license: cc0-1.0 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: esd.list |
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column_names: |
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- file_name |
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- speaker |
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- language |
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- transcription |
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language: |
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- yue |
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tags: |
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- cantonese |
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- audio |
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dataset_info: |
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features: |
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- name: file_name |
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dtype: audio |
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- name: speaker |
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dtype: string |
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- name: language |
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dtype: string |
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- name: transcription |
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dtype: string |
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--- |
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# 張悦楷三國演義 |
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Fork from [laubonghaudoi/zoengjyutgaai_saamgwokjinji](https://huggingface.co/datasets/laubonghaudoi/zoengjyutgaai_saamgwokjinji) |
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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. |
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```python |
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subtitles = [] |
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splits = librosa.effects.split(audio) # shape: (682, 2) |
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!mkdir -p dataset/zoengjyutgaai_saamgwokjinji/wavs |
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# split audio by srt time |
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for i, sub in enumerate(subs): |
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chunk_start = sub.start.to_time() |
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chunk_end = sub.end.to_time() |
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chunk_start = ((chunk_start.minute * 60) + chunk_start.second) * sr |
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chunk_end = ((chunk_end.minute * 60) + chunk_end.second) * sr |
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# Find the closest split |
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chunk_start = min(splits, key=lambda x: abs(x[0] - chunk_start))[0] |
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chunk_end = min(splits, key=lambda x: abs(x[1] - chunk_end))[1] |
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chunk = audio[chunk_start:chunk_end] |
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wav_file = f"001_{i:03}.wav" |
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# resample, since bert-vits2 training only support 44.1k |
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try: |
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chunk = librosa.resample(chunk, sr, 44100) |
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except: |
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print(f"Error resampling {wav_file}") |
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continue |
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subtitles.append({ 'path': wav_file, 'speaker': 'zoengjyutgaai', 'language': 'YUE', 'text': sub.text }) |
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# export audio |
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sf.write(f"dataset/zoengjyutgaai_saamgwokjinji/wavs/{wav_file}", chunk, 44100, subtype='PCM_16') |
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df = pd.DataFrame(subtitles) |
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df.to_csv("dataset/zoengjyutgaai_saamgwokjinji/001.csv", index=False, sep='|', header=False) |
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``` |
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