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