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| import torch | |
| from Layers.ResidualBlock import HiFiGANResidualBlock as ResidualBlock | |
| class HiFiGANGenerator(torch.nn.Module): | |
| def __init__(self, | |
| path_to_weights, | |
| in_channels=80, | |
| out_channels=1, | |
| channels=512, | |
| kernel_size=7, | |
| upsample_scales=(8, 6, 4, 4), | |
| upsample_kernel_sizes=(16, 12, 8, 8), | |
| resblock_kernel_sizes=(3, 7, 11), | |
| resblock_dilations=[(1, 3, 5), (1, 3, 5), (1, 3, 5)], | |
| use_additional_convs=True, | |
| bias=True, | |
| nonlinear_activation="LeakyReLU", | |
| nonlinear_activation_params={"negative_slope": 0.1}, | |
| use_weight_norm=True, ): | |
| super().__init__() | |
| assert kernel_size % 2 == 1, "Kernal size must be odd number." | |
| assert len(upsample_scales) == len(upsample_kernel_sizes) | |
| assert len(resblock_dilations) == len(resblock_kernel_sizes) | |
| self.num_upsamples = len(upsample_kernel_sizes) | |
| self.num_blocks = len(resblock_kernel_sizes) | |
| self.input_conv = torch.nn.Conv1d(in_channels, | |
| channels, | |
| kernel_size, | |
| 1, | |
| padding=(kernel_size - 1) // 2, ) | |
| self.upsamples = torch.nn.ModuleList() | |
| self.blocks = torch.nn.ModuleList() | |
| for i in range(len(upsample_kernel_sizes)): | |
| self.upsamples += [ | |
| torch.nn.Sequential(getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params), | |
| torch.nn.ConvTranspose1d(channels // (2 ** i), | |
| channels // (2 ** (i + 1)), | |
| upsample_kernel_sizes[i], | |
| upsample_scales[i], | |
| padding=(upsample_kernel_sizes[i] - upsample_scales[i]) // 2, ), )] | |
| for j in range(len(resblock_kernel_sizes)): | |
| self.blocks += [ResidualBlock(kernel_size=resblock_kernel_sizes[j], | |
| channels=channels // (2 ** (i + 1)), | |
| dilations=resblock_dilations[j], | |
| bias=bias, | |
| use_additional_convs=use_additional_convs, | |
| nonlinear_activation=nonlinear_activation, | |
| nonlinear_activation_params=nonlinear_activation_params, )] | |
| self.output_conv = torch.nn.Sequential( | |
| torch.nn.LeakyReLU(), | |
| torch.nn.Conv1d(channels // (2 ** (i + 1)), | |
| out_channels, | |
| kernel_size, | |
| 1, | |
| padding=(kernel_size - 1) // 2, ), | |
| torch.nn.Tanh(), ) | |
| if use_weight_norm: | |
| self.apply_weight_norm() | |
| self.load_state_dict(torch.load(path_to_weights, map_location='cpu')["generator"]) | |
| def forward(self, c, normalize_before=False): | |
| if normalize_before: | |
| c = (c - self.mean) / self.scale | |
| c = self.input_conv(c.unsqueeze(0)) | |
| for i in range(self.num_upsamples): | |
| c = self.upsamples[i](c) | |
| cs = 0.0 # initialize | |
| for j in range(self.num_blocks): | |
| cs = cs + self.blocks[i * self.num_blocks + j](c) | |
| c = cs / self.num_blocks | |
| c = self.output_conv(c) | |
| return c.squeeze(0).squeeze(0) | |
| def remove_weight_norm(self): | |
| def _remove_weight_norm(m): | |
| try: | |
| torch.nn.utils.remove_weight_norm(m) | |
| except ValueError: | |
| return | |
| self.apply(_remove_weight_norm) | |
| def apply_weight_norm(self): | |
| def _apply_weight_norm(m): | |
| if isinstance(m, torch.nn.Conv1d) or isinstance(m, torch.nn.ConvTranspose1d): | |
| torch.nn.utils.weight_norm(m) | |
| self.apply(_apply_weight_norm) | |