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import torch
from torch import nn
from torch.nn import functional as F
from nn_layers import convolutional_module
torch.manual_seed(1234)
class Postnet(nn.Module):
"""Postnet
- Five 1-d convolution with 512 channels and kernel size 5
"""
def __init__(self, tacotron_hyperparams):
super(Postnet, self).__init__()
# self.dropout = nn.Dropout(0.5)
self.convolutions = nn.ModuleList()
self.convolutions.append(
nn.Sequential(
convolutional_module(tacotron_hyperparams['n_mel_channels'],
tacotron_hyperparams['postnet_embedding_dim'],
kernel_size=tacotron_hyperparams['postnet_kernel_size'], stride=1,
padding=int((tacotron_hyperparams['postnet_kernel_size'] - 1) / 2),
dilation=1, w_init_gain='tanh'),
nn.BatchNorm1d(tacotron_hyperparams['postnet_embedding_dim']))
)
for i in range(1, tacotron_hyperparams['postnet_n_convolutions'] - 1):
self.convolutions.append(
nn.Sequential(
convolutional_module(tacotron_hyperparams['postnet_embedding_dim'],
tacotron_hyperparams['postnet_embedding_dim'],
kernel_size=tacotron_hyperparams['postnet_kernel_size'], stride=1,
padding=int((tacotron_hyperparams['postnet_kernel_size'] - 1) / 2),
dilation=1, w_init_gain='tanh'),
nn.BatchNorm1d(tacotron_hyperparams['postnet_embedding_dim']))
)
self.convolutions.append(
nn.Sequential(
convolutional_module(tacotron_hyperparams['postnet_embedding_dim'],
tacotron_hyperparams['n_mel_channels'],
kernel_size=tacotron_hyperparams['postnet_kernel_size'], stride=1,
padding=int((tacotron_hyperparams['postnet_kernel_size'] - 1) / 2),
dilation=1, w_init_gain='linear'),
nn.BatchNorm1d(tacotron_hyperparams['n_mel_channels']))
)
def forward(self, x):
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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