import torch import torch.nn as nn import torch.nn.functional as F from torch.nn import Conv1d, ConvTranspose1d from torch.nn.utils import remove_weight_norm, weight_norm from transformers import PreTrainedModel from configuration_hifigan import HiFiGANConfig 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 (kernel_size * dilation - dilation) // 2 class ResBlock(torch.nn.Module): def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)): super(ResBlock, self).__init__() 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 layer in self.convs1: remove_weight_norm(layer) for layer in self.convs2: remove_weight_norm(layer) class HiFiGAN(PreTrainedModel): config_class = HiFiGANConfig def __init__(self, config): super().__init__(config) self.num_kernels = len(config.resblock_kernel_sizes) self.num_upsamples = len(config.upsample_rates) self.conv_pre = weight_norm( Conv1d( config.model_in_dim, config.upsample_initial_channel, 7, 1, padding=3, ) ) self.ups = nn.ModuleList() for i, (u, k) in enumerate( zip(config.upsample_rates, config.upsample_kernel_sizes) ): self.ups.append( weight_norm( ConvTranspose1d( config.upsample_initial_channel // (2**i), config.upsample_initial_channel // (2 ** (i + 1)), k, u, padding=(k - u) // 2, ) ) ) self.resblocks = nn.ModuleList() for i in range(len(self.ups)): ch = config.upsample_initial_channel // (2 ** (i + 1)) for k, d in zip( config.resblock_kernel_sizes, config.resblock_dilation_sizes ): self.resblocks.append(ResBlock(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 layer in self.ups: remove_weight_norm(layer) for layer in self.resblocks: layer.remove_weight_norm() remove_weight_norm(self.conv_pre) remove_weight_norm(self.conv_post)