mirror of
https://github.com/storytold/storyteller-ml.git
synced 2026-10-17 00:23:14 +00:00
Paul Sajna
f86b44d8fc
* 48KHz softvc * add some notes * almost forgot the inference script * fix readme link * requirements.txt
56 lines
1.6 KiB
Python
56 lines
1.6 KiB
Python
from pathlib import Path
|
|
import numpy as np
|
|
|
|
import torch
|
|
import torch.nn.functional as F
|
|
from torch.utils.data import Dataset
|
|
from torch.nn.utils.rnn import pad_sequence
|
|
|
|
|
|
class MelDataset(Dataset):
|
|
def __init__(self, root: Path, train: bool = True, discrete: bool = False):
|
|
self.discrete = discrete
|
|
self.mels_dir = root / "mels"
|
|
self.units_dir = root / "discrete" if discrete else root / "soft"
|
|
|
|
pattern = "train/**/*.npy" if train else "dev/**/*.npy"
|
|
self.metadata = [
|
|
path.relative_to(self.mels_dir)#.with_suffix("")
|
|
for path in self.mels_dir.rglob(pattern)
|
|
]
|
|
|
|
def __len__(self):
|
|
return len(self.metadata)
|
|
|
|
def __getitem__(self, index):
|
|
path = self.metadata[index]
|
|
mel_path = self.mels_dir / path
|
|
units_path = self.units_dir / path
|
|
|
|
mel = np.load(mel_path).T
|
|
units = np.load(units_path)
|
|
|
|
length = 2 * units.shape[0]
|
|
|
|
mel = torch.from_numpy(mel[:length, :])
|
|
mel = F.pad(mel, (0, 0, 1, 0))
|
|
units = torch.from_numpy(units)
|
|
if self.discrete:
|
|
units = units.long()
|
|
return mel, units
|
|
|
|
def pad_collate(self, batch):
|
|
mels, units = zip(*batch)
|
|
|
|
mels, units = list(mels), list(units)
|
|
|
|
mels_lengths = torch.tensor([x.size(0) - 1 for x in mels])
|
|
units_lengths = torch.tensor([x.size(0) for x in units])
|
|
|
|
mels = pad_sequence(mels, batch_first=True)
|
|
units = pad_sequence(
|
|
units, batch_first=True, padding_value=100 if self.discrete else 0
|
|
)
|
|
|
|
return mels, mels_lengths, units, units_lengths
|