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