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import torchaudio |
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import torchaudio.functional as F |
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import glob |
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from pathlib import Path |
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from multiprocessing import Pool |
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import os |
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from functools import partial |
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import numpy as np |
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import torch |
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import tqdm |
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import torch.multiprocessing |
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class TUTRealLoader: |
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def __init__(self): |
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self._fs = 44100 |
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self._eps = np.spacing(np.float64(1e-16)) |
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self._audio_max_len_samples = 30 * self._fs |
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self._nb_channels = 4 |
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def _load(self, audio_path): |
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waveform, fs = torchaudio.load(audio_path, channels_first=False) |
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audio = waveform[:, :self._nb_channels] + self._eps |
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if audio.shape[0] < self._audio_max_len_samples: |
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audio = torch.nn.functional.pad( |
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audio, |
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(0, 0, 0, self._audio_max_len_samples - audio.shape[0]) |
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) |
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elif audio.shape[0] > self._audio_max_len_samples: |
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audio = audio[:self._audio_max_len_samples, :] |
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return audio, fs |
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RESAMPLE_RATE = 32000 |
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PATH = "original_audios" |
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SAVE_PATH = f"audios_sr={RESAMPLE_RATE}" |
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def resample(path, loader, resample_rate, device): |
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waveform, sample_rate = loader._load(path) |
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waveform = waveform.to(device) |
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if waveform.shape[0] != 4: |
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waveform = waveform.T |
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resampled_waveform = F.resample( |
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waveform, |
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sample_rate, |
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resample_rate, |
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lowpass_filter_width=64, |
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rolloff=0.9475937167399596, |
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resampling_method="sinc_interp_kaiser", |
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beta=14.769656459379492, |
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) |
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return resampled_waveform |
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def resample_and_save(audio, resample_rate, loader, device): |
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resampled_audio = resample(audio, loader, resample_rate, device) |
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assert resampled_audio.shape[0] == 4, "Swap channel dimensions" |
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file_name = Path(audio).stem |
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file_ext = Path(audio).suffix |
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save_file = f"{SAVE_PATH}/{file_name}{file_ext}" |
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if not os.path.exists(save_file): |
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torchaudio.save(save_file, resampled_audio.cpu(), resample_rate, channels_first=True) |
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if __name__ == "__main__": |
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torch.multiprocessing.set_start_method('spawn', force=True) |
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os.makedirs(SAVE_PATH, exist_ok=True) |
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device = torch.device("cpu" if not torch.cuda.is_available() else "cuda") |
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loader = TUTRealLoader() |
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audios = glob.glob(f"{PATH}/*.wav") |
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audios = list(filter(lambda x: not os.path.exists(os.path.join(SAVE_PATH, Path(x).stem + ".wav")), audios)) |
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print(f"Found {len(audios)} to resample") |
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p = Pool(8) |
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resample_and_save_partial = partial(resample_and_save, resample_rate = RESAMPLE_RATE, loader=loader, device = device) |
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r = list(tqdm.tqdm(p.imap(resample_and_save_partial, audios), total=len(audios))) |
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p.close() |
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p.join() |
