119 lines
4.8 KiB
Python
119 lines
4.8 KiB
Python
import os
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import random
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import torch
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import torch.utils.data
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import torchaudio
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from tqdm import tqdm
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from data.audio.wav_aug import WavAugmentor
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from data.util import find_files_of_type, is_wav_file
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from models.tacotron2.taco_utils import load_wav_to_torch
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from utils.util import opt_get
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class WavfileDataset(torch.utils.data.Dataset):
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def __init__(self, opt):
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path = opt['path']
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cache_path = opt['cache_path'] # Will fail when multiple paths specified, must be specified in this case.
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if not isinstance(path, list):
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path = [path]
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if os.path.exists(cache_path):
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self.audiopaths = torch.load(cache_path)
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else:
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print("Building cache..")
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self.audiopaths = []
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for p in path:
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self.audiopaths.extend(find_files_of_type('img', p, qualifier=is_wav_file)[0])
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torch.save(self.audiopaths, cache_path)
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# Parse options
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self.sampling_rate = opt_get(opt, ['sampling_rate'], 24000)
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self.augment = opt_get(opt, ['do_augmentation'], False)
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self.pad_to = opt_get(opt, ['pad_to_seconds'], None)
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if self.pad_to is not None:
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self.pad_to *= self.sampling_rate
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self.window = 2 * self.sampling_rate
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if self.augment:
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self.augmentor = WavAugmentor()
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def get_audio_for_index(self, index):
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audiopath = self.audiopaths[index]
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audio, sampling_rate = load_wav_to_torch(audiopath)
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if sampling_rate != self.sampling_rate:
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if sampling_rate < self.sampling_rate:
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print(f'{audiopath} has a sample rate of {sampling_rate} which is lower than the requested sample rate of {self.sampling_rate}. This is not a good idea.')
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audio = torch.nn.functional.interpolate(audio.unsqueeze(0).unsqueeze(1), scale_factor=self.sampling_rate/sampling_rate, mode='nearest', recompute_scale_factor=False).squeeze()
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# Check some assumptions about audio range. This should be automatically fixed in load_wav_to_torch, but might not be in some edge cases, where we should squawk.
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# '2' is arbitrarily chosen since it seems like audio will often "overdrive" the [-1,1] bounds.
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if torch.any(audio > 2) or not torch.any(audio < 0):
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print(f"Error with {audiopath}. Max={audio.max()} min={audio.min()}")
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audio.clip_(-1, 1)
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audio = audio.unsqueeze(0)
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return audio, audiopath
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def __getitem__(self, index):
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clip1, clip2 = None, None
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while clip1 is None and clip2 is None:
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# Split audio_norm into two tensors of equal size.
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audio_norm, filename = self.get_audio_for_index(index)
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if audio_norm.shape[1] < self.window * 2:
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# Try next index. This adds a bit of bias and ideally we'd filter the dataset rather than do this.
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index = (index + 1) % len(self)
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continue
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j = random.randint(0, audio_norm.shape[1] - self.window)
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clip1 = audio_norm[:, j:j+self.window]
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if self.augment:
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clip1 = self.augmentor.augment(clip1, self.sampling_rate)
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j = random.randint(0, audio_norm.shape[1]-self.window)
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clip2 = audio_norm[:, j:j+self.window]
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if self.augment:
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clip2 = self.augmentor.augment(clip2, self.sampling_rate)
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# This is required when training to make sure all clips align.
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if self.pad_to is not None:
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if audio_norm.shape[-1] <= self.pad_to:
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audio_norm = torch.nn.functional.pad(audio_norm, (0, self.pad_to - audio_norm.shape[-1]))
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else:
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#print(f"Warning! Truncating clip {filename} from {audio_norm.shape[-1]} to {self.pad_to}")
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audio_norm = audio_norm[:, :self.pad_to]
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return {
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'clip': audio_norm,
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'clip1': clip1[0, :].unsqueeze(0),
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'clip2': clip2[0, :].unsqueeze(0),
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'path': filename,
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}
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def __len__(self):
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return len(self.audiopaths)
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if __name__ == '__main__':
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params = {
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'mode': 'wavfile_clips',
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'path': ['E:\\audio\\books-split', 'E:\\audio\\LibriTTS\\train-clean-360', 'D:\\data\\audio\\podcasts-split'],
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'cache_path': 'E:\\audio\\clips-cache.pth',
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'sampling_rate': 22050,
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'pad_to_seconds': 5,
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'phase': 'train',
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'n_workers': 0,
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'batch_size': 16,
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}
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from data import create_dataset, create_dataloader, util
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ds = create_dataset(params)
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dl = create_dataloader(ds, params)
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i = 0
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for b in tqdm(dl):
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for b_ in range(16):
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pass
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#torchaudio.save(f'{i}_clip1_{b_}.wav', b['clip1'][b_], ds.sampling_rate)
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#torchaudio.save(f'{i}_clip2_{b_}.wav', b['clip2'][b_], ds.sampling_rate)
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#i += 1
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