DL-Art-School/codes/data/audio/wavfile_dataset.py

93 lines
3.2 KiB
Python
Raw Normal View History

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 = torch.autograd.Variable(audio_norm, requires_grad=False)
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[0] < 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[0] - 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[0]-self.window)
clip2 = audio_norm[j:j+self.window]
if self.augment:
clip2 = self.augmentor.augment(clip2, self.sampling_rate)
return {
'clip1': clip1.unsqueeze(0),
'clip2': clip2.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, c = create_dataset(params, return_collate=True)
dl = create_dataloader(ds, params, collate_fn=c)
i = 0
for b in tqdm(dl):
torchaudio.save(f'{i}_clip1.wav', b['clip1'], ds.sampling_rate)
torchaudio.save(f'{i}_clip2.wav', b['clip2'], ds.sampling_rate)
i += 1