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