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import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from utils import ConvNorm, LinearNorm
from torch.nn.parameter import Parameter
class GroupNorm_Mask(nn.Module):
def __init__(self, num_groups, num_channels, eps=1e-5, affine=True):
super().__init__()
self.num_groups = num_groups
self.num_channels = num_channels
self.eps = eps
self.affine = affine
if self.affine:
self.weight = Parameter(torch.Tensor(num_channels))
self.bias = Parameter(torch.Tensor(num_channels))
else:
self.register_parameter('weight', None)
self.register_parameter('bias', None)
self.reset_parameters()
def reset_parameters(self):
if self.affine:
nn.init.ones_(self.weight)
nn.init.zeros_(self.bias)
def forward(self, x, mask):
B, C, L = x.size()
assert C % self.num_groups == 0
x = x.view(B, self.num_groups, C//self.num_groups, L)
mask = mask.view(B, 1, 1, L)
x = x * mask
mean = x.mean(dim=2, keepdim=True).sum(dim=3, keepdim=True) / mask.sum(dim=3, keepdim=True)
var = (((x - mean)**2)*mask).mean(dim=2, keepdim=True).sum(dim=3, keepdim=True) / mask.sum(dim=3, keepdim=True)
x = (x - mean) / (var + self.eps).sqrt()
x = x.view(B, C, L)
return x * self.weight.view(1,-1,1) + self.bias.view(1,-1,1)
class M43_Sequential(nn.Sequential):
def forward(self, inputs, mask):
inputs = self._modules['0'](inputs)
inputs = self._modules['1'](inputs, mask)
return inputs
class Encoder(nn.Module):
"""Encoder module:
"""
def __init__(self, hparams):
super(Encoder, self).__init__()
self.dim_freq = hparams.dim_freq_sea
self.dim_enc = hparams.dim_enc_sea
self.chs_grp = hparams.chs_grp
self.dim_neck = hparams.dim_neck_sea
convolutions = []
for i in range(5):
conv_layer = M43_Sequential(
ConvNorm(self.dim_freq if i==0 else self.dim_enc,
self.dim_enc,
kernel_size=1, stride=1,
padding=0,
dilation=1, w_init_gain='relu'),
GroupNorm_Mask(self.dim_enc//self.chs_grp, self.dim_enc))
convolutions.append(conv_layer)
conv_layer = M43_Sequential(
ConvNorm(self.dim_enc,
128,
kernel_size=1, stride=1,
padding=0,
dilation=1, w_init_gain='relu'),
GroupNorm_Mask(128//self.chs_grp, 128))
convolutions.append(conv_layer)
conv_layer = M43_Sequential(
ConvNorm(128,
32,
kernel_size=1, stride=1,
padding=0,
dilation=1, w_init_gain='relu'),
GroupNorm_Mask(32//self.chs_grp, 32))
convolutions.append(conv_layer)
conv_layer = M43_Sequential(
ConvNorm(32,
self.dim_neck,
kernel_size=1, stride=1,
padding=0,
dilation=1, w_init_gain='relu'),
GroupNorm_Mask(1, self.dim_neck))
convolutions.append(conv_layer)
self.convolutions = nn.ModuleList(convolutions)
def forward(self, x, mask):
for conv in self.convolutions:
x = F.relu(conv(x, mask))
codes = x.permute(0, 2, 1) * mask.unsqueeze(-1)
return codes
class Decoder(nn.Module):
"""Decoder module:
"""
def __init__(self, hparams):
super(Decoder, self).__init__()
self.dim_enc = hparams.dim_enc_sea
self.dim_emb = hparams.dim_spk
self.dim_freq = hparams.dim_freq_sp
self.dim_neck = hparams.dim_neck_sea
self.lstm = nn.LSTM(self.dim_neck+self.dim_emb,
1024, 3, batch_first=True)
self.linear_projection = LinearNorm(1024, self.dim_freq)
def forward(self, x):
outputs = self.lstm(x)[0]
decoder_output = self.linear_projection(outputs)
return decoder_output
class Generator(nn.Module):
"""Generator network."""
def __init__(self, hparams):
super(Generator, self).__init__()
self.encoder = Encoder(hparams)
self.decoder = Decoder(hparams)
def forward(self, x, c_trg):
x = x.transpose(2,1)
codes = self.encoder(x)
encoder_outputs = torch.cat((codes,
c_trg.unsqueeze(1).expand(-1,x.size(-1),-1)), dim=-1)
mel_outputs = self.decoder(encoder_outputs)
return mel_outputs
def encode(self, x, mask):
x = x.transpose(2,1)
codes = self.encoder(x, mask)
return codes
def decode(self, codes, c_trg):
encoder_outputs = torch.cat((codes,
c_trg.unsqueeze(1).expand(-1,codes.size(1),-1)), dim=-1)
mel_outputs = self.decoder(encoder_outputs)
return mel_outputs
class Encoder_2(nn.Module):
"""Encoder module:
"""
def __init__(self, hparams):
super().__init__()
self.dim_freq = hparams.dim_freq_sea
self.dim_enc = hparams.dim_enc_sea
self.chs_grp = hparams.chs_grp
self.dim_neck = hparams.dim_neck_sea
convolutions = []
for i in range(5):
conv_layer = M43_Sequential(
ConvNorm(self.dim_freq if i==0 else self.dim_enc,
self.dim_enc,
kernel_size=5, stride=1,
padding=2,
dilation=1, w_init_gain='relu'),
GroupNorm_Mask(self.dim_enc//self.chs_grp, self.dim_enc))
convolutions.append(conv_layer)
conv_layer = M43_Sequential(
ConvNorm(self.dim_enc,
128,
kernel_size=5, stride=1,
padding=2,
dilation=1, w_init_gain='relu'),
GroupNorm_Mask(128//self.chs_grp, 128))
convolutions.append(conv_layer)
conv_layer = M43_Sequential(
ConvNorm(128,
32,
kernel_size=5, stride=1,
padding=2,
dilation=1, w_init_gain='relu'),
GroupNorm_Mask(32//self.chs_grp, 32))
convolutions.append(conv_layer)
conv_layer = M43_Sequential(
ConvNorm(32,
self.dim_neck,
kernel_size=5, stride=1,
padding=2,
dilation=1, w_init_gain='linear'),
GroupNorm_Mask(1, self.dim_neck))
convolutions.append(conv_layer)
self.convolutions = nn.ModuleList(convolutions)
def forward(self, x, mask):
for i in range(len(self.convolutions)-1):
x = F.relu(self.convolutions[i](x, mask))
x = self.convolutions[-1](x, mask)
codes = x.permute(0, 2, 1) * mask.unsqueeze(-1)
return codes |