import torch import torch.nn.functional as F import torch.nn as nn from torch.nn import Conv1d, ConvTranspose1d from torch.nn.utils import weight_norm, remove_weight_norm LRELU_SLOPE = 0.1 def init_weights(m, mean=0.0, std=0.01): classname = m.__class__.__name__ if classname.find("Conv") != -1: m.weight.data.normal_(mean, std) def get_padding(kernel_size, dilation=1): return int((kernel_size * dilation - dilation) / 2) class ResBlock1(torch.nn.Module): def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)): 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]), ) ), ] ) 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), ) ), ] ) 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 Generator(torch.nn.Module): def __init__(self, h): super(Generator, self).__init__() self.h = h self.num_kernels = len(h.resblock_kernel_sizes) self.num_upsamples = len(h.upsample_rates) self.conv_pre = weight_norm( Conv1d(256, h.upsample_initial_channel, 7, 1, padding=3) ) resblock = ResBlock1 if h.resblock == "1" else ResBlock2 self.ups = nn.ModuleList() for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)): self.ups.append( weight_norm( ConvTranspose1d( h.upsample_initial_channel // (2**i), h.upsample_initial_channel // (2 ** (i + 1)), u * 2, u, padding=u // 2 + u % 2, output_padding=u % 2, ) ) ) 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) def forward(self, x): # import ipdb; ipdb.set_trace() x = self.conv_pre(x) for i in range(self.num_upsamples): 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 x = F.leaky_relu(x) 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() remove_weight_norm(self.conv_pre) remove_weight_norm(self.conv_post) ################################################################################################## # import torch # import torch.nn as nn # import torch.nn.functional as F # from torch.nn import Conv1d, ConvTranspose1d # from torch.nn.utils import weight_norm, remove_weight_norm # LRELU_SLOPE = 0.1 # def init_weights(m, mean=0.0, std=0.01): # classname = m.__class__.__name__ # if classname.find("Conv") != -1: # m.weight.data.normal_(mean, std) # def get_padding(kernel_size, dilation=1): # return int((kernel_size * dilation - dilation) / 2) # class ResBlock(torch.nn.Module): # def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)): # super(ResBlock, 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]), # ) # ), # ] # ) # 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), # ) # ), # ] # ) # 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 Generator(torch.nn.Module): # def __init__(self, h): # super(Generator, self).__init__() # self.h = h # self.num_kernels = len(h.resblock_kernel_sizes) # self.num_upsamples = len(h.upsample_rates) # self.conv_pre = weight_norm( # Conv1d(h.num_mels, h.upsample_initial_channel, 7, 1, padding=3) # ) # resblock = ResBlock # self.ups = nn.ModuleList() # for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)): # 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.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) # def forward(self, x): # x = self.conv_pre(x) # for i in range(self.num_upsamples): # 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 # x = F.leaky_relu(x) # 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() # remove_weight_norm(self.conv_pre) # remove_weight_norm(self.conv_post)