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import torchaudio
import torchaudio.functional as F
import glob
from pathlib import Path
from multiprocessing import Pool
import os
from functools import partial
import torch
import tqdm
import torch.multiprocessing
RESAMPLE_RATE = 32000
PATH = "original_audios"
SAVE_PATH = f"audios_sr={RESAMPLE_RATE}"
def resample(path, resample_rate, device):
waveform, sample_rate = torchaudio.load(path, channels_first=False)
waveform = waveform.to(device)
if waveform.shape[0] != 4:
waveform = waveform.T
resampled_waveform = F.resample(
waveform,
sample_rate,
resample_rate,
lowpass_filter_width=64,
rolloff=0.9475937167399596,
resampling_method="sinc_interp_kaiser",
beta=14.769656459379492,
)
return resampled_waveform
def resample_and_save(audio, resample_rate, device):
resampled_audio = resample(audio, resample_rate, device)
assert resampled_audio.shape[0] == 4, "Swap channel dimensions"
file_name = Path(audio).stem
file_ext = Path(audio).suffix
save_file = f"{SAVE_PATH}/{file_name}{file_ext}"
if not os.path.exists(save_file):
torchaudio.save(save_file, resampled_audio.cpu(), resample_rate, channels_first=True)
if __name__ == "__main__":
torch.multiprocessing.set_start_method('spawn', force=True)
os.makedirs(SAVE_PATH, exist_ok=True)
device = torch.device("cpu" if not torch.cuda.is_available() else "cuda")
audios = glob.glob(f"{PATH}/*.wav")
audios = list(filter(lambda x: not os.path.exists(os.path.join(SAVE_PATH, Path(x).stem + ".wav")), audios))
print(f"Found {len(audios)} to resample")
p = Pool(8)
resample_and_save_partial = partial(resample_and_save, resample_rate = RESAMPLE_RATE, device = device)
r = list(tqdm.tqdm(p.imap(resample_and_save_partial, audios), total=len(audios)))
p.close()
p.join() |