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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
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