| import torchaudio |
| import torchaudio.functional as F |
| import glob |
| from pathlib import Path |
| from multiprocessing import Pool |
| import os |
| from functools import partial |
|
|
| import numpy as np |
| import torch |
| import tqdm |
|
|
| import torch.multiprocessing |
|
|
| class TUTRealLoader: |
| def __init__(self): |
| self._fs = 44100 |
| self._eps = np.spacing(np.float64(1e-16)) |
| self._audio_max_len_samples = 30 * self._fs |
| self._nb_channels = 4 |
|
|
| def _load(self, audio_path): |
| waveform, fs = torchaudio.load(audio_path, channels_first=False) |
| audio = waveform[:, :self._nb_channels] + self._eps |
| |
| |
| if audio.shape[0] < self._audio_max_len_samples: |
| audio = torch.nn.functional.pad( |
| audio, |
| (0, 0, 0, self._audio_max_len_samples - audio.shape[0]) |
| ) |
| elif audio.shape[0] > self._audio_max_len_samples: |
| audio = audio[:self._audio_max_len_samples, :] |
|
|
| return audio, fs |
|
|
| RESAMPLE_RATE = 32000 |
| PATH = "original_audios" |
| SAVE_PATH = f"audios_sr={RESAMPLE_RATE}" |
|
|
| def resample(path, loader, resample_rate, device): |
| waveform, sample_rate = loader._load(path) |
| 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, loader, device): |
| resampled_audio = resample(audio, loader, 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") |
| loader = TUTRealLoader() |
| 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, loader=loader, device = device) |
| r = list(tqdm.tqdm(p.imap(resample_and_save_partial, audios), total=len(audios))) |
| p.close() |
| p.join() |
