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| # Copyright (c) 2022 NVIDIA CORPORATION. | |
| # Licensed under the MIT license. | |
| # Adapted from https://github.com/jik876/hifi-gan under the MIT license. | |
| # LICENSE is in incl_licenses directory. | |
| import torch | |
| import torch.nn as nn | |
| from torch.nn import Conv1d, ConvTranspose1d | |
| from torch.nn.utils.parametrizations import weight_norm | |
| from torch.nn.utils.parametrize import remove_parametrizations | |
| from mmaudio.ext.bigvgan import activations | |
| from mmaudio.ext.bigvgan.alias_free_torch import * | |
| from mmaudio.ext.bigvgan.utils import get_padding, init_weights | |
| LRELU_SLOPE = 0.1 | |
| class AMPBlock1(torch.nn.Module): | |
| def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5), activation=None): | |
| super(AMPBlock1, 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) | |
| 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=activations.Snake(channels, alpha_logscale=h.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=activations.SnakeBeta(channels, alpha_logscale=h.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_parametrizations(l, 'weight') | |
| for l in self.convs2: | |
| remove_parametrizations(l, 'weight') | |
| class AMPBlock2(torch.nn.Module): | |
| def __init__(self, h, channels, kernel_size=3, dilation=(1, 3), activation=None): | |
| super(AMPBlock2, 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) | |
| 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=activations.Snake(channels, alpha_logscale=h.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=activations.SnakeBeta(channels, alpha_logscale=h.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_parametrizations(l, 'weight') | |
| class BigVGANVocoder(torch.nn.Module): | |
| # this is our main BigVGAN model. Applies anti-aliased periodic activation for resblocks. | |
| def __init__(self, h): | |
| super().__init__() | |
| self.h = h | |
| self.num_kernels = len(h.resblock_kernel_sizes) | |
| self.num_upsamples = len(h.upsample_rates) | |
| # pre conv | |
| self.conv_pre = weight_norm(Conv1d(h.num_mels, h.upsample_initial_channel, 7, 1, padding=3)) | |
| # define which AMPBlock to use. BigVGAN uses AMPBlock1 as default | |
| resblock = AMPBlock1 if h.resblock == '1' else AMPBlock2 | |
| # transposed conv-based upsamplers. does not apply anti-aliasing | |
| self.ups = nn.ModuleList() | |
| for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)): | |
| self.ups.append( | |
| nn.ModuleList([ | |
| weight_norm( | |
| ConvTranspose1d(h.upsample_initial_channel // (2**i), | |
| h.upsample_initial_channel // (2**(i + 1)), | |
| k, | |
| u, | |
| padding=(k - u) // 2)) | |
| ])) | |
| # residual blocks using anti-aliased multi-periodicity composition modules (AMP) | |
| 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, activation=h.activation)) | |
| # post conv | |
| if h.activation == "snake": # periodic nonlinearity with snake function and anti-aliasing | |
| activation_post = activations.Snake(ch, alpha_logscale=h.snake_logscale) | |
| self.activation_post = Activation1d(activation=activation_post) | |
| elif h.activation == "snakebeta": # periodic nonlinearity with snakebeta function and anti-aliasing | |
| activation_post = activations.SnakeBeta(ch, alpha_logscale=h.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 initialization | |
| for i in range(len(self.ups)): | |
| self.ups[i].apply(init_weights) | |
| self.conv_post.apply(init_weights) | |
| def forward(self, x): | |
| # pre conv | |
| x = self.conv_pre(x) | |
| for i in range(self.num_upsamples): | |
| # upsampling | |
| for i_up in range(len(self.ups[i])): | |
| x = self.ups[i][i_up](x) | |
| # AMP blocks | |
| 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 | |
| # post conv | |
| 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_parametrizations(l_i, 'weight') | |
| for l in self.resblocks: | |
| l.remove_weight_norm() | |
| remove_parametrizations(self.conv_pre, 'weight') | |
| remove_parametrizations(self.conv_post, 'weight') | |