mirror of
https://github.com/babysor/MockingBird.git
synced 2024-03-22 13:11:31 +08:00
210 lines
8.0 KiB
Python
210 lines
8.0 KiB
Python
|
import torch
|
||
|
|
import torch.nn.functional as F
|
||
|
|
import torch.nn as nn
|
||
|
|
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
|
||
|
|
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
|
||
|
|
from vocoder.fregan.utils import init_weights, get_padding
|
||
|
|
|
||
|
|
LRELU_SLOPE = 0.1
|
||
|
|
|
||
|
|
|
||
|
|
class ResBlock1(torch.nn.Module):
|
||
|
|
def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5, 7)):
|
||
|
|
super(ResBlock1, self).__init__()
|
||
|
|
self.h = h
|
||
|
|
self.convs1 = nn.ModuleList([
|
||
|
|
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
|
||
|
|
padding=get_padding(kernel_size, dilation[0]))),
|
||
|
|
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
|
||
|
|
padding=get_padding(kernel_size, dilation[1]))),
|
||
|
|
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
|
||
|
|
padding=get_padding(kernel_size, dilation[2]))),
|
||
|
|
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[3],
|
||
|
|
padding=get_padding(kernel_size, dilation[3])))
|
||
|
|
])
|
||
|
|
self.convs1.apply(init_weights)
|
||
|
|
|
||
|
|
self.convs2 = nn.ModuleList([
|
||
|
|
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
||
|
|
padding=get_padding(kernel_size, 1))),
|
||
|
|
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
||
|
|
padding=get_padding(kernel_size, 1))),
|
||
|
|
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
||
|
|
padding=get_padding(kernel_size, 1))),
|
||
|
|
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
||
|
|
padding=get_padding(kernel_size, 1)))
|
||
|
|
])
|
||
|
|
self.convs2.apply(init_weights)
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
for c1, c2 in zip(self.convs1, self.convs2):
|
||
|
|
xt = F.leaky_relu(x, LRELU_SLOPE)
|
||
|
|
xt = c1(xt)
|
||
|
|
xt = F.leaky_relu(xt, LRELU_SLOPE)
|
||
|
|
xt = c2(xt)
|
||
|
|
x = xt + x
|
||
|
|
return x
|
||
|
|
|
||
|
|
def remove_weight_norm(self):
|
||
|
|
for l in self.convs1:
|
||
|
|
remove_weight_norm(l)
|
||
|
|
for l in self.convs2:
|
||
|
|
remove_weight_norm(l)
|
||
|
|
|
||
|
|
|
||
|
|
class ResBlock2(torch.nn.Module):
|
||
|
|
def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)):
|
||
|
|
super(ResBlock2, self).__init__()
|
||
|
|
self.h = h
|
||
|
|
self.convs = nn.ModuleList([
|
||
|
|
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
|
||
|
|
padding=get_padding(kernel_size, dilation[0]))),
|
||
|
|
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
|
||
|
|
padding=get_padding(kernel_size, dilation[1])))
|
||
|
|
])
|
||
|
|
self.convs.apply(init_weights)
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
for c in self.convs:
|
||
|
|
xt = F.leaky_relu(x, LRELU_SLOPE)
|
||
|
|
xt = c(xt)
|
||
|
|
x = xt + x
|
||
|
|
return x
|
||
|
|
|
||
|
|
def remove_weight_norm(self):
|
||
|
|
for l in self.convs:
|
||
|
|
remove_weight_norm(l)
|
||
|
|
|
||
|
|
|
||
|
|
class FreGAN(torch.nn.Module):
|
||
|
|
def __init__(self, h, top_k=4):
|
||
|
|
super(FreGAN, self).__init__()
|
||
|
|
self.h = h
|
||
|
|
|
||
|
|
self.num_kernels = len(h.resblock_kernel_sizes)
|
||
|
|
self.num_upsamples = len(h.upsample_rates)
|
||
|
|
self.upsample_rates = h.upsample_rates
|
||
|
|
self.up_kernels = h.upsample_kernel_sizes
|
||
|
|
self.cond_level = self.num_upsamples - top_k
|
||
|
|
self.conv_pre = weight_norm(Conv1d(80, h.upsample_initial_channel, 7, 1, padding=3))
|
||
|
|
resblock = ResBlock1 if h.resblock == '1' else ResBlock2
|
||
|
|
|
||
|
|
self.ups = nn.ModuleList()
|
||
|
|
self.cond_up = nn.ModuleList()
|
||
|
|
self.res_output = nn.ModuleList()
|
||
|
|
upsample_ = 1
|
||
|
|
kr = 80
|
||
|
|
|
||
|
|
for i, (u, k) in enumerate(zip(self.upsample_rates, self.up_kernels)):
|
||
|
|
# self.ups.append(weight_norm(
|
||
|
|
# ConvTranspose1d(h.upsample_initial_channel // (2 ** i), h.upsample_initial_channel // (2 ** (i + 1)),
|
||
|
|
# k, u, padding=(k - u) // 2)))
|
||
|
|
self.ups.append(weight_norm(ConvTranspose1d(h.upsample_initial_channel//(2**i),
|
||
|
|
h.upsample_initial_channel//(2**(i+1)),
|
||
|
|
k, u, padding=(u//2 + u%2), output_padding=u%2)))
|
||
|
|
|
||
|
|
if i > (self.num_upsamples - top_k):
|
||
|
|
self.res_output.append(
|
||
|
|
nn.Sequential(
|
||
|
|
nn.Upsample(scale_factor=u, mode='nearest'),
|
||
|
|
weight_norm(nn.Conv1d(h.upsample_initial_channel // (2 ** i),
|
||
|
|
h.upsample_initial_channel // (2 ** (i + 1)), 1))
|
||
|
|
)
|
||
|
|
)
|
||
|
|
if i >= (self.num_upsamples - top_k):
|
||
|
|
self.cond_up.append(
|
||
|
|
weight_norm(
|
||
|
|
ConvTranspose1d(kr, h.upsample_initial_channel // (2 ** i),
|
||
|
|
self.up_kernels[i - 1], self.upsample_rates[i - 1],
|
||
|
|
padding=(self.upsample_rates[i-1]//2+self.upsample_rates[i-1]%2), output_padding=self.upsample_rates[i-1]%2))
|
||
|
|
)
|
||
|
|
kr = h.upsample_initial_channel // (2 ** i)
|
||
|
|
|
||
|
|
upsample_ *= u
|
||
|
|
|
||
|
|
self.resblocks = nn.ModuleList()
|
||
|
|
for i in range(len(self.ups)):
|
||
|
|
ch = h.upsample_initial_channel // (2 ** (i + 1))
|
||
|
|
for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)):
|
||
|
|
self.resblocks.append(resblock(h, ch, k, d))
|
||
|
|
|
||
|
|
self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
|
||
|
|
self.ups.apply(init_weights)
|
||
|
|
self.conv_post.apply(init_weights)
|
||
|
|
self.cond_up.apply(init_weights)
|
||
|
|
self.res_output.apply(init_weights)
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
mel = x
|
||
|
|
x = self.conv_pre(x)
|
||
|
|
output = None
|
||
|
|
for i in range(self.num_upsamples):
|
||
|
|
if i >= self.cond_level:
|
||
|
|
mel = self.cond_up[i - self.cond_level](mel)
|
||
|
|
x += mel
|
||
|
|
if i > self.cond_level:
|
||
|
|
if output is None:
|
||
|
|
output = self.res_output[i - self.cond_level - 1](x)
|
||
|
|
else:
|
||
|
|
output = self.res_output[i - self.cond_level - 1](output)
|
||
|
|
x = F.leaky_relu(x, LRELU_SLOPE)
|
||
|
|
x = self.ups[i](x)
|
||
|
|
xs = None
|
||
|
|
for j in range(self.num_kernels):
|
||
|
|
if xs is None:
|
||
|
|
xs = self.resblocks[i * self.num_kernels + j](x)
|
||
|
|
else:
|
||
|
|
xs += self.resblocks[i * self.num_kernels + j](x)
|
||
|
|
x = xs / self.num_kernels
|
||
|
|
if output is not None:
|
||
|
|
output = output + x
|
||
|
|
|
||
|
|
x = F.leaky_relu(output)
|
||
|
|
x = self.conv_post(x)
|
||
|
|
x = torch.tanh(x)
|
||
|
|
|
||
|
|
return x
|
||
|
|
|
||
|
|
def remove_weight_norm(self):
|
||
|
|
print('Removing weight norm...')
|
||
|
|
for l in self.ups:
|
||
|
|
remove_weight_norm(l)
|
||
|
|
for l in self.resblocks:
|
||
|
|
l.remove_weight_norm()
|
||
|
|
for l in self.cond_up:
|
||
|
|
remove_weight_norm(l)
|
||
|
|
for l in self.res_output:
|
||
|
|
remove_weight_norm(l[1])
|
||
|
|
remove_weight_norm(self.conv_pre)
|
||
|
|
remove_weight_norm(self.conv_post)
|
||
|
|
|
||
|
|
|
||
|
|
'''
|
||
|
|
to run this, fix
|
||
|
|
from . import ResStack
|
||
|
|
into
|
||
|
|
from res_stack import ResStack
|
||
|
|
'''
|
||
|
|
if __name__ == '__main__':
|
||
|
|
'''
|
||
|
|
torch.Size([3, 80, 10])
|
||
|
|
torch.Size([3, 1, 2000])
|
||
|
|
4527362
|
||
|
|
'''
|
||
|
|
with open('config.json') as f:
|
||
|
|
data = f.read()
|
||
|
|
from utils import AttrDict
|
||
|
|
import json
|
||
|
|
json_config = json.loads(data)
|
||
|
|
h = AttrDict(json_config)
|
||
|
|
model = FreGAN(h)
|
||
|
|
|
||
|
|
c = torch.randn(3, 80, 10) # (B, channels, T).
|
||
|
|
print(c.shape)
|
||
|
|
|
||
|
|
y = model(c) # (B, 1, T ** prod(upsample_scales)
|
||
|
|
print(y.shape)
|
||
|
|
assert y.shape == torch.Size([3, 1, 2560]) # For normal melgan torch.Size([3, 1, 2560])
|
||
|
|
|
||
|
|
pytorch_total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
|
||
|
|
print(pytorch_total_params)
|