Datasets:
File size: 3,322 Bytes
8fcf91d 5304b62 8fcf91d 5304b62 8fcf91d 5304b62 8fcf91d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 |
from multiprocessing import Pool
from typing import List, Literal
import numpy as np
import torchaudio
from datasets import concatenate_datasets, load_dataset
from pandas import concat, 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
def get_info(subsets: List[str] | Literal["saamgwokjinji", "seoiwuzyun"]) -> None:
"""
計音頻長度、字數、平均字數、中位數字數、覆蓋字數、平均語速
"""
if not isinstance(subsets, list):
subsets = [subsets]
datasets = []
for subset in subsets:
dataset = load_dataset('audiofolder', data_dir=f'./opus/{subset}', split='train')
datasets.append(dataset)
if len(datasets) > 1:
dataset = concatenate_datasets(datasets)
else:
dataset = datasets[0]
# Get all file paths
file_paths = [item['audio']['path'] for item in dataset]
# Use a process pool to calculate durations in parallel
with Pool() as p:
durations = list(tqdm(p.imap(get_audio_length, file_paths), total=len(file_paths)))
total_duration = sum(durations)
# Calculate statistics
min_duration = min(durations)
max_duration = max(durations)
avg_duration = np.mean(durations)
median_duration = np.median(durations)
# Print results
print(f"Statistics for: {' & '.join(subsets)}")
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")
# Concatenate metadata DataFrames
metadata_list = []
for subset in subsets:
metadata = read_csv(f'./opus/{subset}/metadata.csv')
metadata_list.append(metadata)
if len(metadata_list) > 1:
metadata = concat(metadata_list)
else:
metadata = metadata_list[0]
# Calculate total number of characters
total_characters = metadata['transcription'].str.len().sum()
# Calculate mean number of characters
average_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"Average characters per clip: {average_characters:.2f}")
print(f"Median characters per transcription: {median_characters}")
print(f"Number of unique characters: {len(unique_characters)}")
print(f"Average speech rate: {total_characters / total_duration:.2f} characters per second")
print(f"Unique characters: {''.join(sorted(unique_characters))}")
print("-" * 20)
if __name__ == '__main__':
# get_info('saamgwokjinji')
# get_info('seoiwuzyun')
# get_info('mouzaakdung')
get_info('lukdinggei')
# get_info(['saamgwokjinji', 'seoiwuzyun', 'mouzaakdung', 'lukdinggei'])
|