mirror of
https://github.com/babysor/MockingBird.git
synced 2024-03-22 13:11:31 +08:00
53 lines
1.8 KiB
Python
53 lines
1.8 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
from .basic_layers import Linear, Conv1d
|
|
|
|
|
|
class Postnet(nn.Module):
|
|
"""Postnet
|
|
- Five 1-d convolution with 512 channels and kernel size 5
|
|
"""
|
|
def __init__(self, num_mels=80,
|
|
num_layers=5,
|
|
hidden_dim=512,
|
|
kernel_size=5):
|
|
super(Postnet, self).__init__()
|
|
self.convolutions = nn.ModuleList()
|
|
|
|
self.convolutions.append(
|
|
nn.Sequential(
|
|
Conv1d(
|
|
num_mels, hidden_dim,
|
|
kernel_size=kernel_size, stride=1,
|
|
padding=int((kernel_size - 1) / 2),
|
|
dilation=1, w_init_gain='tanh'),
|
|
nn.BatchNorm1d(hidden_dim)))
|
|
|
|
for i in range(1, num_layers - 1):
|
|
self.convolutions.append(
|
|
nn.Sequential(
|
|
Conv1d(
|
|
hidden_dim,
|
|
hidden_dim,
|
|
kernel_size=kernel_size, stride=1,
|
|
padding=int((kernel_size - 1) / 2),
|
|
dilation=1, w_init_gain='tanh'),
|
|
nn.BatchNorm1d(hidden_dim)))
|
|
|
|
self.convolutions.append(
|
|
nn.Sequential(
|
|
Conv1d(
|
|
hidden_dim, num_mels,
|
|
kernel_size=kernel_size, stride=1,
|
|
padding=int((kernel_size - 1) / 2),
|
|
dilation=1, w_init_gain='linear'),
|
|
nn.BatchNorm1d(num_mels)))
|
|
|
|
def forward(self, x):
|
|
# x: (B, num_mels, T_dec)
|
|
for i in range(len(self.convolutions) - 1):
|
|
x = F.dropout(torch.tanh(self.convolutions[i](x)), 0.5, self.training)
|
|
x = F.dropout(self.convolutions[-1](x), 0.5, self.training)
|
|
return x
|