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Running
on
Zero
| import typing as tp | |
| import torch | |
| import torch.nn as nn | |
| from torch.nn import Conv1d, ConvTranspose1d | |
| from torch.nn.utils import weight_norm, remove_weight_norm | |
| from fireredtts.modules.bigvgan.alias_free_torch import ( | |
| Activation1d as TorchActivation1d, | |
| ) | |
| from fireredtts.modules.bigvgan.activations import Snake, SnakeBeta | |
| 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 AMPBlock1(torch.nn.Module): | |
| def __init__( | |
| self, | |
| channels, | |
| kernel_size=3, | |
| dilation=(1, 3, 5), | |
| activation=None, | |
| snake_logscale=True, | |
| use_cuda_kernel=False, | |
| ): | |
| super(AMPBlock1, 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) | |
| self.num_layers = len(self.convs1) + len( | |
| self.convs2 | |
| ) # total number of conv layers | |
| # select which Activation1d, lazy-load cuda version to ensure backward compatibility | |
| if use_cuda_kernel: | |
| from modules.bigvgan.alias_free_cuda.activation1d import ( | |
| Activation1d as CudaActivation1d, | |
| ) | |
| Activation1d = CudaActivation1d | |
| else: | |
| Activation1d = TorchActivation1d | |
| if ( | |
| activation == "snake" | |
| ): # periodic nonlinearity with snake function and anti-aliasing | |
| self.activations = nn.ModuleList( | |
| [ | |
| Activation1d( | |
| activation=Snake(channels, alpha_logscale=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=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, | |
| channels, | |
| kernel_size=3, | |
| dilation=(1, 3), | |
| activation=None, | |
| snake_logscale=True, | |
| use_cuda_kernel=False, | |
| ): | |
| super(AMPBlock2, self).__init__() | |
| 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 | |
| # select which Activation1d, lazy-load cuda version to ensure backward compatibility | |
| if use_cuda_kernel: | |
| from modules.bigvgan.alias_free_cuda.activation1d import ( | |
| Activation1d as CudaActivation1d, | |
| ) | |
| Activation1d = CudaActivation1d | |
| else: | |
| Activation1d = TorchActivation1d | |
| if ( | |
| activation == "snake" | |
| ): # periodic nonlinearity with snake function and anti-aliasing | |
| self.activations = nn.ModuleList( | |
| [ | |
| Activation1d( | |
| activation=Snake(channels, alpha_logscale=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=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): | |
| # this is our main BigVGAN model. Applies anti-aliased periodic activation for resblocks. | |
| def __init__( | |
| self, | |
| num_mels: int, | |
| upsample_initial_channel: int, | |
| resblock_kernel_sizes: tp.List[int], | |
| resblock_dilation_sizes: tp.List[tp.List[int]], | |
| upsample_rates: tp.List[int], | |
| upsample_kernel_sizes: tp.List[int], | |
| resblock_type: str = "1", | |
| snake_logscale: bool = True, | |
| activation: str = "snakebeta", | |
| use_tanh_at_final: bool = False, | |
| use_bias_at_final: bool = False, | |
| use_cuda_kernel: bool = False, | |
| ): | |
| super(BigVGAN, self).__init__() | |
| self.num_kernels = len(resblock_kernel_sizes) | |
| self.num_upsamples = len(upsample_rates) | |
| # pre conv | |
| self.conv_pre = weight_norm( | |
| Conv1d(num_mels, upsample_initial_channel, 7, 1, padding=3) | |
| ) | |
| # define which AMPBlock to use. BigVGAN uses AMPBlock1 as default | |
| resblock = AMPBlock1 if resblock_type == "1" else AMPBlock2 | |
| # transposed conv-based upsamplers. does not apply anti-aliasing | |
| self.ups = nn.ModuleList() | |
| for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): | |
| self.ups.append( | |
| nn.ModuleList( | |
| [ | |
| weight_norm( | |
| ConvTranspose1d( | |
| upsample_initial_channel // (2**i), | |
| 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 = upsample_initial_channel // (2 ** (i + 1)) | |
| for j, (k, d) in enumerate( | |
| zip(resblock_kernel_sizes, resblock_dilation_sizes) | |
| ): | |
| self.resblocks.append( | |
| resblock( | |
| ch, | |
| k, | |
| d, | |
| activation=activation, | |
| snake_logscale=snake_logscale, | |
| use_cuda_kernel=use_cuda_kernel, | |
| ) | |
| ) | |
| # select which Activation1d, lazy-load cuda version to ensure backward compatibility | |
| if use_cuda_kernel: | |
| from modules.bigvgan.alias_free_cuda.activation1d import ( | |
| Activation1d as CudaActivation1d, | |
| ) | |
| Activation1d = CudaActivation1d | |
| else: | |
| Activation1d = TorchActivation1d | |
| # post conv | |
| if ( | |
| activation == "snake" | |
| ): # periodic nonlinearity with snake function and anti-aliasing | |
| activation_post = Snake(ch, alpha_logscale=snake_logscale) | |
| self.activation_post = Activation1d(activation=activation_post) | |
| elif ( | |
| activation == "snakebeta" | |
| ): # periodic nonlinearity with snakebeta function and anti-aliasing | |
| activation_post = SnakeBeta(ch, alpha_logscale=snake_logscale) | |
| self.activation_post = Activation1d(activation=activation_post) | |
| else: | |
| raise NotImplementedError( | |
| "activation incorrectly specified. check the config file and look for 'activation'." | |
| ) | |
| # whether to use bias for the final conv_post. Defaults to True for backward compatibility | |
| self.use_bias_at_final = use_bias_at_final | |
| self.conv_post = weight_norm( | |
| Conv1d(ch, 1, 7, 1, padding=3, bias=self.use_bias_at_final) | |
| ) | |
| # weight initialization | |
| for i in range(len(self.ups)): | |
| self.ups[i].apply(init_weights) | |
| self.conv_post.apply(init_weights) | |
| # final tanh activation. Defaults to True for backward compatibility | |
| self.use_tanh_at_final = use_tanh_at_final | |
| 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) | |
| # final tanh activation | |
| if self.use_tanh_at_final: | |
| x = torch.tanh(x) | |
| else: | |
| x = torch.clamp(x, min=-1.0, max=1.0) # bound the output to [-1, 1] | |
| 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) | |