Spaces:
Build error
Build error
File size: 2,437 Bytes
4ee33aa |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 |
import json
import os
from pathlib import Path
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.data
import torchaudio
import torchvision
from tqdm import tqdm
from ttts.classifier.infer import read_jsonl
class PreprocessedMelDataset(torch.utils.data.Dataset):
def __init__(self, opt):
# cache_path = opt['dataset']['cache_path'] # Will fail when multiple paths specified, must be specified in this case.
# if os.path.exists(cache_path):
# self.paths = torch.load(cache_path)
# else:
# print("Building cache..")
# path = Path(path)
# self.paths = [str(p) for p in path.rglob("*.mel.pth")]
# torch.save(self.paths, cache_path)
paths = read_jsonl(opt['dataset']['path'])
pre = os.path.expanduser(opt['dataset']['pre'])
self.paths = [os.path.join(pre,d['path'])+'.mel.pth' for d in paths]
self.pad_to = opt['dataset']['pad_to_samples']
self.squeeze = opt['dataset']['should_squeeze']
def __getitem__(self, index):
try:
mel = torch.load(self.paths[index])
except:
mel = torch.zeros(1,100,self.pad_to)
if mel.shape[-1] >= self.pad_to:
start = torch.randint(0, mel.shape[-1] - self.pad_to+1, (1,))
mel = mel[:, :, start:start+self.pad_to]
mask = torch.zeros_like(mel)
else:
mask = torch.zeros_like(mel)
padding_needed = self.pad_to - mel.shape[-1]
mel = F.pad(mel, (0,padding_needed))
mask = F.pad(mask, (0,padding_needed), value=1)
assert mel.shape[-1] == self.pad_to
if self.squeeze:
mel = mel.squeeze()
return mel
def __len__(self):
return len(self.paths)
if __name__ == '__main__':
params = {
'mode': 'preprocessed_mel',
'path': 'Y:\\separated\\large_mel_cheaters',
'cache_path': 'Y:\\separated\\large_mel_cheaters_win.pth',
'pad_to_samples': 646,
'phase': 'train',
'n_workers': 0,
'batch_size': 16,
}
cfg = json.load(open('vqvae/config.json'))
ds = PreprocessedMelDataset(cfg)
dl = torch.utils.data.DataLoader(ds, **cfg['dataloader'])
i = 0
for b in tqdm(dl):
#pass
torchvision.utils.save_image((b['mel']+1)/2, f'{i}.png')
i += 1
if i > 20:
break |