mirror of
https://github.com/babysor/MockingBird.git
synced 2024-03-22 13:11:31 +08:00
b617a87ee4
* Init ppg extractor and ppg2mel * add preprocess and training * FIx known issues * Update __init__.py Allow to gen audio * Fix length issue * Fix bug of preparing fid * Fix sample issues * Add UI usage of PPG-vc
119 lines
3.8 KiB
Python
119 lines
3.8 KiB
Python
from typing import Optional
|
|
from typing import Tuple
|
|
from typing import Union
|
|
|
|
import torch
|
|
|
|
from .nets_utils import make_pad_mask
|
|
|
|
|
|
class Stft(torch.nn.Module):
|
|
def __init__(
|
|
self,
|
|
n_fft: int = 512,
|
|
win_length: Union[int, None] = 512,
|
|
hop_length: int = 128,
|
|
center: bool = True,
|
|
pad_mode: str = "reflect",
|
|
normalized: bool = False,
|
|
onesided: bool = True,
|
|
kaldi_padding_mode=False,
|
|
):
|
|
super().__init__()
|
|
self.n_fft = n_fft
|
|
if win_length is None:
|
|
self.win_length = n_fft
|
|
else:
|
|
self.win_length = win_length
|
|
self.hop_length = hop_length
|
|
self.center = center
|
|
self.pad_mode = pad_mode
|
|
self.normalized = normalized
|
|
self.onesided = onesided
|
|
self.kaldi_padding_mode = kaldi_padding_mode
|
|
if self.kaldi_padding_mode:
|
|
self.win_length = 400
|
|
|
|
def extra_repr(self):
|
|
return (
|
|
f"n_fft={self.n_fft}, "
|
|
f"win_length={self.win_length}, "
|
|
f"hop_length={self.hop_length}, "
|
|
f"center={self.center}, "
|
|
f"pad_mode={self.pad_mode}, "
|
|
f"normalized={self.normalized}, "
|
|
f"onesided={self.onesided}"
|
|
)
|
|
|
|
def forward(
|
|
self, input: torch.Tensor, ilens: torch.Tensor = None
|
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
|
|
"""STFT forward function.
|
|
|
|
Args:
|
|
input: (Batch, Nsamples) or (Batch, Nsample, Channels)
|
|
ilens: (Batch)
|
|
Returns:
|
|
output: (Batch, Frames, Freq, 2) or (Batch, Frames, Channels, Freq, 2)
|
|
|
|
"""
|
|
bs = input.size(0)
|
|
if input.dim() == 3:
|
|
multi_channel = True
|
|
# input: (Batch, Nsample, Channels) -> (Batch * Channels, Nsample)
|
|
input = input.transpose(1, 2).reshape(-1, input.size(1))
|
|
else:
|
|
multi_channel = False
|
|
|
|
# output: (Batch, Freq, Frames, 2=real_imag)
|
|
# or (Batch, Channel, Freq, Frames, 2=real_imag)
|
|
if not self.kaldi_padding_mode:
|
|
output = torch.stft(
|
|
input,
|
|
n_fft=self.n_fft,
|
|
win_length=self.win_length,
|
|
hop_length=self.hop_length,
|
|
center=self.center,
|
|
pad_mode=self.pad_mode,
|
|
normalized=self.normalized,
|
|
onesided=self.onesided,
|
|
return_complex=False
|
|
)
|
|
else:
|
|
# NOTE(sx): Use Kaldi-fasion padding, maybe wrong
|
|
num_pads = self.n_fft - self.win_length
|
|
input = torch.nn.functional.pad(input, (num_pads, 0))
|
|
output = torch.stft(
|
|
input,
|
|
n_fft=self.n_fft,
|
|
win_length=self.win_length,
|
|
hop_length=self.hop_length,
|
|
center=False,
|
|
pad_mode=self.pad_mode,
|
|
normalized=self.normalized,
|
|
onesided=self.onesided,
|
|
return_complex=False
|
|
)
|
|
|
|
# output: (Batch, Freq, Frames, 2=real_imag)
|
|
# -> (Batch, Frames, Freq, 2=real_imag)
|
|
output = output.transpose(1, 2)
|
|
if multi_channel:
|
|
# output: (Batch * Channel, Frames, Freq, 2=real_imag)
|
|
# -> (Batch, Frame, Channel, Freq, 2=real_imag)
|
|
output = output.view(bs, -1, output.size(1), output.size(2), 2).transpose(
|
|
1, 2
|
|
)
|
|
|
|
if ilens is not None:
|
|
if self.center:
|
|
pad = self.win_length // 2
|
|
ilens = ilens + 2 * pad
|
|
olens = torch.div(ilens - self.win_length, self.hop_length, rounding_mode='floor') + 1
|
|
# olens = ilens - self.win_length // self.hop_length + 1
|
|
output.masked_fill_(make_pad_mask(olens, output, 1), 0.0)
|
|
else:
|
|
olens = None
|
|
|
|
return output, olens
|