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import argparse
import os
from pathlib import Path
import pandas as pd
import torchaudio
from tqdm import tqdm
min_length_sec = 8.1
max_segments_per_clip = 5
parser = argparse.ArgumentParser(description='Process audio clips.')
parser.add_argument('--data_dir',
type=Path,
help='Path to the directory containing audio files',
default='./training/example_audios')
parser.add_argument('--output_dir',
type=Path,
help='Path to the output tsv file',
default='./training/example_output/clips.tsv')
parser.add_argument('--start', type=int, help='Start index for processing files', default=0)
parser.add_argument('--end', type=int, help='End index for processing files', default=-1)
args = parser.parse_args()
data_dir = args.data_dir
output_dir = args.output_dir
start = args.start
end = args.end
output_data = []
blacklisted = 0
if end == -1:
end = len(os.listdir(data_dir))
audio_files = sorted(os.listdir(data_dir))[start:end]
print(f'Processing {len(audio_files)} files from {start} to {end}')
for audio_file in tqdm(audio_files):
audio_file_path = data_dir / audio_file
audio_name = audio_file_path.stem
waveform, sample_rate = torchaudio.load(audio_file_path)
# waveform: (1/2) * length
if waveform.shape[1] < sample_rate * min_length_sec:
continue
# try to partition the audio into segments, each with length of min_length_sec
segment_length = int(sample_rate * min_length_sec)
total_length = waveform.shape[1]
num_segments = min(max_segments_per_clip, total_length // segment_length)
if num_segments > 1:
segment_interval = (total_length - segment_length) // (num_segments - 1)
else:
segment_interval = 0
for i in range(num_segments):
start_sample = i * segment_interval
end_sample = start_sample + segment_length
audio_id = f'{audio_name}_{i}'
output_data.append((audio_id, audio_name, start_sample, end_sample))
output_dir.parent.mkdir(parents=True, exist_ok=True)
output_df = pd.DataFrame(output_data, columns=['id', 'name', 'start_sample', 'end_sample'])
output_df.to_csv(output_dir, index=False, sep='\t')
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