99 lines
3.3 KiB
Python
99 lines
3.3 KiB
Python
import argparse
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import concurrent.futures
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import os
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from concurrent.futures import ProcessPoolExecutor
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from multiprocessing import cpu_count
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import librosa
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import numpy as np
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from rich.progress import track
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from scipy.io import wavfile
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def load_wav(wav_path):
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return librosa.load(wav_path, sr=None)
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def trim_wav(wav, top_db=40):
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return librosa.effects.trim(wav, top_db=top_db)
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def normalize_peak(wav, threshold=1.0):
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peak = np.abs(wav).max()
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if peak > threshold:
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wav = 0.98 * wav / peak
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return wav
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def resample_wav(wav, sr, target_sr):
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return librosa.resample(wav, orig_sr=sr, target_sr=target_sr)
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def save_wav_to_path(wav, save_path, sr):
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wavfile.write(
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save_path,
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sr,
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(wav * np.iinfo(np.int16).max).astype(np.int16)
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)
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def process(item):
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spkdir, wav_name, args = item
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speaker = spkdir.replace("\\", "/").split("/")[-1]
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wav_path = os.path.join(args.in_dir, speaker, wav_name)
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if os.path.exists(wav_path) and '.wav' in wav_path:
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os.makedirs(os.path.join(args.out_dir2, speaker), exist_ok=True)
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wav, sr = load_wav(wav_path)
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wav, _ = trim_wav(wav)
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wav = normalize_peak(wav)
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resampled_wav = resample_wav(wav, sr, args.sr2)
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if not args.skip_loudnorm:
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resampled_wav /= np.max(np.abs(resampled_wav))
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save_path2 = os.path.join(args.out_dir2, speaker, wav_name)
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save_wav_to_path(resampled_wav, save_path2, args.sr2)
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"""
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def process_all_speakers():
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process_count = 30 if os.cpu_count() > 60 else (os.cpu_count() - 2 if os.cpu_count() > 4 else 1)
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with ThreadPoolExecutor(max_workers=process_count) as executor:
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for speaker in speakers:
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spk_dir = os.path.join(args.in_dir, speaker)
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if os.path.isdir(spk_dir):
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print(spk_dir)
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futures = [executor.submit(process, (spk_dir, i, args)) for i in os.listdir(spk_dir) if i.endswith("wav")]
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for _ in tqdm(concurrent.futures.as_completed(futures), total=len(futures)):
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pass
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"""
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# multi process
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def process_all_speakers():
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process_count = 30 if os.cpu_count() > 60 else (os.cpu_count() - 2 if os.cpu_count() > 4 else 1)
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with ProcessPoolExecutor(max_workers=process_count) as executor:
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for speaker in speakers:
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spk_dir = os.path.join(args.in_dir, speaker)
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if os.path.isdir(spk_dir):
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print(spk_dir)
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futures = [executor.submit(process, (spk_dir, i, args)) for i in os.listdir(spk_dir) if i.endswith("wav")]
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for _ in track(concurrent.futures.as_completed(futures), total=len(futures), description="resampling:"):
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pass
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--sr2", type=int, default=44100, help="sampling rate")
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parser.add_argument("--in_dir", type=str, default="./dataset_raw", help="path to source dir")
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parser.add_argument("--out_dir2", type=str, default="./dataset/44k", help="path to target dir")
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parser.add_argument("--skip_loudnorm", action="store_true", help="Skip loudness matching if you have done it")
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args = parser.parse_args()
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print(f"CPU count: {cpu_count()}")
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speakers = os.listdir(args.in_dir)
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process_all_speakers()
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