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| 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) |