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| # Copyright (c) 2023 Amphion. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import torch | |
| import torch.nn as nn | |
| from torch.nn import Conv1d, ConvTranspose1d | |
| from torch.nn.utils import weight_norm, remove_weight_norm | |
| from modules.vocoder_blocks import * | |
| from modules.activation_functions import * | |
| from modules.anti_aliasing import * | |
| LRELU_SLOPE = 0.1 | |
| # The AMPBlock Module is adopted from BigVGAN under the MIT License | |
| # https://github.com/NVIDIA/BigVGAN | |
| class AMPBlock1(torch.nn.Module): | |
| def __init__( | |
| self, cfg, channels, kernel_size=3, dilation=(1, 3, 5), activation=None | |
| ): | |
| super(AMPBlock1, self).__init__() | |
| self.cfg = cfg | |
| 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) | |
| self.num_layers = len(self.convs1) + len( | |
| self.convs2 | |
| ) # total number of conv layers | |
| if ( | |
| activation == "snake" | |
| ): # periodic nonlinearity with snake function and anti-aliasing | |
| self.activations = nn.ModuleList( | |
| [ | |
| Activation1d( | |
| activation=Snake( | |
| channels, alpha_logscale=cfg.model.bigvgan.snake_logscale | |
| ) | |
| ) | |
| for _ in range(self.num_layers) | |
| ] | |
| ) | |
| elif ( | |
| activation == "snakebeta" | |
| ): # periodic nonlinearity with snakebeta function and anti-aliasing | |
| self.activations = nn.ModuleList( | |
| [ | |
| Activation1d( | |
| activation=SnakeBeta( | |
| channels, alpha_logscale=cfg.model.bigvgan.snake_logscale | |
| ) | |
| ) | |
| for _ in range(self.num_layers) | |
| ] | |
| ) | |
| else: | |
| raise NotImplementedError( | |
| "activation incorrectly specified. check the config file and look for 'activation'." | |
| ) | |
| def forward(self, x): | |
| acts1, acts2 = self.activations[::2], self.activations[1::2] | |
| for c1, c2, a1, a2 in zip(self.convs1, self.convs2, acts1, acts2): | |
| xt = a1(x) | |
| xt = c1(xt) | |
| xt = a2(xt) | |
| 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 AMPBlock2(torch.nn.Module): | |
| def __init__(self, cfg, channels, kernel_size=3, dilation=(1, 3), activation=None): | |
| super(AMPBlock2, self).__init__() | |
| self.cfg = cfg | |
| 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) | |
| self.num_layers = len(self.convs) # total number of conv layers | |
| if ( | |
| activation == "snake" | |
| ): # periodic nonlinearity with snake function and anti-aliasing | |
| self.activations = nn.ModuleList( | |
| [ | |
| Activation1d( | |
| activation=Snake( | |
| channels, alpha_logscale=cfg.model.bigvgan.snake_logscale | |
| ) | |
| ) | |
| for _ in range(self.num_layers) | |
| ] | |
| ) | |
| elif ( | |
| activation == "snakebeta" | |
| ): # periodic nonlinearity with snakebeta function and anti-aliasing | |
| self.activations = nn.ModuleList( | |
| [ | |
| Activation1d( | |
| activation=SnakeBeta( | |
| channels, alpha_logscale=cfg.model.bigvgan.snake_logscale | |
| ) | |
| ) | |
| for _ in range(self.num_layers) | |
| ] | |
| ) | |
| else: | |
| raise NotImplementedError( | |
| "activation incorrectly specified. check the config file and look for 'activation'." | |
| ) | |
| def forward(self, x): | |
| for c, a in zip(self.convs, self.activations): | |
| xt = a(x) | |
| xt = c(xt) | |
| x = xt + x | |
| return x | |
| def remove_weight_norm(self): | |
| for l in self.convs: | |
| remove_weight_norm(l) | |
| class BigVGAN(torch.nn.Module): | |
| def __init__(self, cfg): | |
| super(BigVGAN, self).__init__() | |
| self.cfg = cfg | |
| self.num_kernels = len(cfg.model.bigvgan.resblock_kernel_sizes) | |
| self.num_upsamples = len(cfg.model.bigvgan.upsample_rates) | |
| # Conv pre to boost channels | |
| self.conv_pre = weight_norm( | |
| Conv1d( | |
| cfg.preprocess.n_mel, | |
| cfg.model.bigvgan.upsample_initial_channel, | |
| 7, | |
| 1, | |
| padding=3, | |
| ) | |
| ) | |
| resblock = AMPBlock1 if cfg.model.bigvgan.resblock == "1" else AMPBlock2 | |
| # Upsamplers | |
| self.ups = nn.ModuleList() | |
| for i, (u, k) in enumerate( | |
| zip( | |
| cfg.model.bigvgan.upsample_rates, | |
| cfg.model.bigvgan.upsample_kernel_sizes, | |
| ) | |
| ): | |
| self.ups.append( | |
| nn.ModuleList( | |
| [ | |
| weight_norm( | |
| ConvTranspose1d( | |
| cfg.model.bigvgan.upsample_initial_channel // (2**i), | |
| cfg.model.bigvgan.upsample_initial_channel | |
| // (2 ** (i + 1)), | |
| k, | |
| u, | |
| padding=(k - u) // 2, | |
| ) | |
| ) | |
| ] | |
| ) | |
| ) | |
| # Res Blocks with AMP and Anti-aliasing | |
| self.resblocks = nn.ModuleList() | |
| for i in range(len(self.ups)): | |
| ch = cfg.model.bigvgan.upsample_initial_channel // (2 ** (i + 1)) | |
| for j, (k, d) in enumerate( | |
| zip( | |
| cfg.model.bigvgan.resblock_kernel_sizes, | |
| cfg.model.bigvgan.resblock_dilation_sizes, | |
| ) | |
| ): | |
| self.resblocks.append( | |
| resblock(cfg, ch, k, d, activation=cfg.model.bigvgan.activation) | |
| ) | |
| # Conv post for result | |
| if cfg.model.bigvgan.activation == "snake": | |
| activation_post = Snake(ch, alpha_logscale=cfg.model.bigvgan.snake_logscale) | |
| self.activation_post = Activation1d(activation=activation_post) | |
| elif cfg.model.bigvgan.activation == "snakebeta": | |
| activation_post = SnakeBeta( | |
| ch, alpha_logscale=cfg.model.bigvgan.snake_logscale | |
| ) | |
| self.activation_post = Activation1d(activation=activation_post) | |
| else: | |
| raise NotImplementedError( | |
| "activation incorrectly specified. check the config file and look for 'activation'." | |
| ) | |
| self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3)) | |
| # Weight Norm | |
| for i in range(len(self.ups)): | |
| self.ups[i].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): | |
| for i_up in range(len(self.ups[i])): | |
| x = self.ups[i][i_up](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 = self.activation_post(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: | |
| for l_i in l: | |
| remove_weight_norm(l_i) | |
| for l in self.resblocks: | |
| l.remove_weight_norm() | |
| remove_weight_norm(self.conv_pre) | |
| remove_weight_norm(self.conv_post) | |