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import torch
import torch.nn as nn
import torch.nn.functional as F
# import numpy as np
'''
reference from: https://github.com/auspicious3000/autovc/blob/master/model_vc.py
'''
class LinearNorm(torch.nn.Module):
def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'):
super(LinearNorm, self).__init__()
self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias)
torch.nn.init.xavier_uniform_(
self.linear_layer.weight,
gain=torch.nn.init.calculate_gain(w_init_gain))
def forward(self, x):
return self.linear_layer(x)
class ConvNorm(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
padding=None, dilation=1, bias=True, w_init_gain='linear'):
super(ConvNorm, self).__init__()
if padding is None:
assert(kernel_size % 2 == 1)
padding = int(dilation * (kernel_size - 1) / 2)
self.conv = torch.nn.Conv1d(in_channels, out_channels,
kernel_size=kernel_size, stride=stride,
padding=padding, dilation=dilation,
bias=bias)
torch.nn.init.xavier_uniform_(
self.conv.weight, gain=torch.nn.init.calculate_gain(w_init_gain))
def forward(self, signal):
conv_signal = self.conv(signal)
return conv_signal
class Postnet(nn.Module):
"""Postnet
- Five 1-d convolution with 512 channels and kernel size 5
"""
def __init__(self):
super(Postnet, self).__init__()
self.convolutions = nn.ModuleList()
self.convolutions.append(
nn.Sequential(
ConvNorm(80, 512,
kernel_size=5, stride=1,
padding=2,
dilation=1, w_init_gain='tanh'),
nn.BatchNorm1d(512))
)
for i in range(1, 5 - 1):
self.convolutions.append(
nn.Sequential(
ConvNorm(512,
512,
kernel_size=5, stride=1,
padding=2,
dilation=1, w_init_gain='tanh'),
nn.BatchNorm1d(512))
)
self.convolutions.append(
nn.Sequential(
ConvNorm(512, 80,
kernel_size=5, stride=1,
padding=2,
dilation=1, w_init_gain='linear'),
nn.BatchNorm1d(80))
)
def forward(self, x):
for i in range(len(self.convolutions) - 1):
x = torch.tanh(self.convolutions[i](x))
x = self.convolutions[-1](x)
return x
class Decoder(nn.Module):
"""Decoder module:
"""
def __init__(self, dim_neck=64, dim_lf0=1, dim_emb=256, dim_pre=512):
super(Decoder, self).__init__()
self.lstm1 = nn.LSTM(dim_neck+dim_emb+dim_lf0, dim_pre, 1, batch_first=True)
convolutions = []
for i in range(3):
conv_layer = nn.Sequential(
ConvNorm(dim_pre,
dim_pre,
kernel_size=5, stride=1,
padding=2,
dilation=1, w_init_gain='relu'),
nn.BatchNorm1d(dim_pre))
convolutions.append(conv_layer)
self.convolutions = nn.ModuleList(convolutions)
self.lstm2 = nn.LSTM(dim_pre, 1024, 2, batch_first=True)
self.linear_projection = LinearNorm(1024, 80)
def forward(self, x):
#self.lstm1.flatten_parameters()
x, _ = self.lstm1(x)
x = x.transpose(1, 2)
for conv in self.convolutions:
x = F.relu(conv(x))
x = x.transpose(1, 2)
outputs, _ = self.lstm2(x)
decoder_output = self.linear_projection(outputs)
return decoder_output
class Decoder_ac(nn.Module):
"""Decoder_ac network."""
def __init__(self, dim_neck=64, dim_lf0=1, dim_emb=256, dim_pre=512, use_l1_loss=False):
super(Decoder_ac, self).__init__()
self.use_l1_loss = use_l1_loss
# self.encoder = Encoder(dim_neck, dim_emb, freq)
self.decoder = Decoder(dim_neck, dim_lf0, dim_emb, dim_pre)
self.postnet = Postnet()
def forward(self, z, lf0_embs, spk_embs, mel_target=None):
z = F.interpolate(z.transpose(1, 2), scale_factor=2) # (bs, 140/2, 64) -> (bs, 64, 140/2) -> (bs, 64, 140)
z = z.transpose(1, 2) # (bs, 64, 140) -> (bs, 140, 64)
spk_embs_exp = spk_embs.unsqueeze(1).expand(-1,z.shape[1],-1)
lf0_embs = lf0_embs[:,:z.shape[1],:]
# print(z.shape, lf0_embs.shape)
x = torch.cat([z, lf0_embs, spk_embs_exp], dim=-1)
mel_outputs = self.decoder(x)
mel_outputs_postnet = self.postnet(mel_outputs.transpose(2,1))
mel_outputs_postnet = mel_outputs + mel_outputs_postnet.transpose(2,1)
# print('mel_outputs.shape:', mel_outputs_postnet.shape)
if mel_target is None:
return mel_outputs_postnet
else:
# mel_target = mel_target[:,1:-1,:]
loss = F.mse_loss(mel_outputs, mel_target) + \
F.mse_loss(mel_outputs_postnet, mel_target)
if self.use_l1_loss:
loss = loss + F.l1_loss(mel_outputs, mel_target) + \
F.l1_loss(mel_outputs_postnet, mel_target)
return loss, mel_outputs_postnet
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