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import numpy as np |
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import torchaudio |
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from datasets import load_dataset |
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from pandas import read_csv |
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from tqdm import tqdm |
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def get_audio_length(file_path: str) -> float: |
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""" |
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計錄音總長度 |
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""" |
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metadata = torchaudio.info(file_path) |
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return metadata.num_frames / metadata.sample_rate |
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if __name__ == '__main__': |
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dataset = load_dataset('audiofolder', data_dir='./wav') |
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durations = [] |
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total_duration = 0 |
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for item in tqdm(dataset['train']): |
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file_path = item['audio']['path'] |
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duration = get_audio_length(file_path) |
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durations.append(duration) |
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total_duration += duration |
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min_duration = min(durations) |
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max_duration = max(durations) |
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avg_duration = np.mean(durations) |
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median_duration = np.median(durations) |
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print(f"Total audio duration: {total_duration / 3600:.2f} hours") |
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print(f"Total audio duration: {total_duration / 60:.2f} minutes") |
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print(f"Minimum audio duration: {min_duration:.3f} seconds") |
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print(f"Maximum audio duration: {max_duration:.3f} seconds") |
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print(f"Average audio duration: {avg_duration:.3f} seconds") |
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print(f"Median audio duration: {median_duration:.3f} seconds") |
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metadata = read_csv('./wav/metadata.csv') |
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transcriptions = metadata['transcription'] |
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total_characters = metadata['transcription'].str.len().sum() |
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mean_characters = metadata['transcription'].str.len().mean() |
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median_characters = metadata['transcription'].str.len().median() |
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unique_characters = set(''.join(metadata['transcription'])) |
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print(f"Total characters: {total_characters}") |
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print(f"Mean characters per transcription: {mean_characters:.2f}") |
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print(f"Median characters per transcription: {median_characters}") |
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print(f"Number of unique characters: {len(unique_characters)}") |
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print(f"Unique characters: {''.join(sorted(unique_characters))}") |
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print(f"Average speech rate: {total_characters / total_duration:.2f} characters per second") |
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