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--- |
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dataset_info: |
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features: |
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- name: audio |
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dtype: |
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audio: |
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sampling_rate: 16000 |
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- name: sentence |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 12290918193 |
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num_examples: 42566 |
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download_size: 10107309830 |
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dataset_size: 12290918193 |
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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: data/train-* |
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license: apache-2.0 |
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task_categories: |
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- automatic-speech-recognition |
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language: |
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- ja |
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tags: |
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- audio |
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- nlp |
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- asr |
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size_categories: |
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- 10K<n<100K |
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--- |
|
|
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<pre><code> |
|
|
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dataset = load_dataset("sin2piusc/CSS10_jsss_jvs_cv") |
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output_file = 'metadata.csv' |
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|
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special_characters = '[,♬「」 ？！;:“%‘” ~♪…～ ！?!？#$%&゛＃＄％＆（）＊＋：；〈＝〉？＠＾＿‘｛｜｝～".,]' |
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|
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for i, sample in tqdm(enumerate(dataset)): # Process each sample in the filtered dataset |
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audio_sample = name + f'_{i}.mp3' |
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audio_path = os.path.join(folder_path, audio_sample) |
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transcription_path = os.path.join(folder_path, output_file) # Path to save transcription file |
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sf.write(audio_path, sample['audio']['array'], sample['audio']['sampling_rate']) # Save mp3/wav files |
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sample["audio_length"] = len(sample["audio"]["array"]) / sample["audio"]["sampling_rate"] # Get audio length |
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with open(transcription_path, 'a', encoding='utf-8') as transcription_file: # Save transcription file |
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sample["sentence"] = re.sub(special_characters,'', sample["sentence"]) # Remove special characters |
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transcription_file.write(audio_sample + ",") # Save transcription file name |
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transcription_file.write(sample['sentence']) # Save transcription |
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transcription_file.write(str(","+str(sample['audio_length']))) # Save audio length |
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transcription_file.write('\n') |
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</code></pre> |
