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
78 lines
2.1 KiB
Python
78 lines
2.1 KiB
Python
"""VGG2L definition for transformer-transducer."""
|
|
|
|
import torch
|
|
|
|
|
|
class VGG2L(torch.nn.Module):
|
|
"""VGG2L module for transformer-transducer encoder."""
|
|
|
|
def __init__(self, idim, odim):
|
|
"""Construct a VGG2L object.
|
|
|
|
Args:
|
|
idim (int): dimension of inputs
|
|
odim (int): dimension of outputs
|
|
|
|
"""
|
|
super(VGG2L, self).__init__()
|
|
|
|
self.vgg2l = torch.nn.Sequential(
|
|
torch.nn.Conv2d(1, 64, 3, stride=1, padding=1),
|
|
torch.nn.ReLU(),
|
|
torch.nn.Conv2d(64, 64, 3, stride=1, padding=1),
|
|
torch.nn.ReLU(),
|
|
torch.nn.MaxPool2d((3, 2)),
|
|
torch.nn.Conv2d(64, 128, 3, stride=1, padding=1),
|
|
torch.nn.ReLU(),
|
|
torch.nn.Conv2d(128, 128, 3, stride=1, padding=1),
|
|
torch.nn.ReLU(),
|
|
torch.nn.MaxPool2d((2, 2)),
|
|
)
|
|
|
|
self.output = torch.nn.Linear(128 * ((idim // 2) // 2), odim)
|
|
|
|
def forward(self, x, x_mask):
|
|
"""VGG2L forward for x.
|
|
|
|
Args:
|
|
x (torch.Tensor): input torch (B, T, idim)
|
|
x_mask (torch.Tensor): (B, 1, T)
|
|
|
|
Returns:
|
|
x (torch.Tensor): input torch (B, sub(T), attention_dim)
|
|
x_mask (torch.Tensor): (B, 1, sub(T))
|
|
|
|
"""
|
|
x = x.unsqueeze(1)
|
|
x = self.vgg2l(x)
|
|
|
|
b, c, t, f = x.size()
|
|
|
|
x = self.output(x.transpose(1, 2).contiguous().view(b, t, c * f))
|
|
|
|
if x_mask is None:
|
|
return x, None
|
|
else:
|
|
x_mask = self.create_new_mask(x_mask, x)
|
|
|
|
return x, x_mask
|
|
|
|
def create_new_mask(self, x_mask, x):
|
|
"""Create a subsampled version of x_mask.
|
|
|
|
Args:
|
|
x_mask (torch.Tensor): (B, 1, T)
|
|
x (torch.Tensor): (B, sub(T), attention_dim)
|
|
|
|
Returns:
|
|
x_mask (torch.Tensor): (B, 1, sub(T))
|
|
|
|
"""
|
|
x_t1 = x_mask.size(2) - (x_mask.size(2) % 3)
|
|
x_mask = x_mask[:, :, :x_t1][:, :, ::3]
|
|
|
|
x_t2 = x_mask.size(2) - (x_mask.size(2) % 2)
|
|
x_mask = x_mask[:, :, :x_t2][:, :, ::2]
|
|
|
|
return x_mask
|