import os import random import torch import torch.utils.data import torchaudio from tqdm import tqdm from data.audio.wav_aug import WavAugmentor from data.util import get_image_paths, is_wav_file from models.tacotron2.taco_utils import load_wav_to_torch from utils.util import opt_get class WavfileDataset(torch.utils.data.Dataset): def __init__(self, opt): self.path = os.path.dirname(opt['path']) cache_path = os.path.join(self.path, 'cache.pth') if os.path.exists(cache_path): self.audiopaths = torch.load(cache_path) else: print("Building cache..") self.audiopaths = get_image_paths('img', opt['path'], qualifier=is_wav_file)[0] torch.save(self.audiopaths, cache_path) # Parse options self.sampling_rate = opt_get(opt, ['sampling_rate'], 24000) self.augment = opt_get(opt, ['do_augmentation'], False) self.max_wav_value = 32768.0 self.window = 2 * self.sampling_rate if self.augment: self.augmentor = WavAugmentor() def get_audio_for_index(self, index): audiopath = self.audiopaths[index] filename = os.path.join(self.path, audiopath) audio, sampling_rate = load_wav_to_torch(filename) if sampling_rate != self.sampling_rate: raise ValueError(f"Input sampling rate does not match specified rate {self.sampling_rate}") audio_norm = audio / self.max_wav_value audio_norm = audio_norm.unsqueeze(0) return audio_norm, audiopath def __getitem__(self, index): clip1, clip2 = None, None while clip1 is None and clip2 is None: # Split audio_norm into two tensors of equal size. audio_norm, filename = self.get_audio_for_index(index) if audio_norm.shape[1] < self.window * 2: # Try next index. This adds a bit of bias and ideally we'd filter the dataset rather than do this. index = (index + 1) % len(self) continue j = random.randint(0, audio_norm.shape[1] - self.window) clip1 = audio_norm[:, j:j+self.window] if self.augment: clip1 = self.augmentor.augment(clip1, self.sampling_rate) j = random.randint(0, audio_norm.shape[1]-self.window) clip2 = audio_norm[:, j:j+self.window] if self.augment: clip2 = self.augmentor.augment(clip2, self.sampling_rate) return { 'clip1': clip1[0, :].unsqueeze(0), 'clip2': clip2[0, :].unsqueeze(0), 'path': filename, } def __len__(self): return len(self.audiopaths) if __name__ == '__main__': params = { 'mode': 'wavfile_clips', 'path': 'E:\\audio\\LibriTTS\\train-other-500', 'phase': 'train', 'n_workers': 0, 'batch_size': 16, 'do_augmentation': True, } from data import create_dataset, create_dataloader, util ds = create_dataset(params, return_collate=True) dl = create_dataloader(ds, params, collate_fn=c) i = 0 for b in tqdm(dl): for b_ in range(16): torchaudio.save(f'{i}_clip1_{b_}.wav', b['clip1'][b_], ds.sampling_rate) torchaudio.save(f'{i}_clip2_{b_}.wav', b['clip2'][b_], ds.sampling_rate) i += 1