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Running
on
Zero
| # 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.functional as F | |
| import torch.nn as nn | |
| from torch.nn import Conv1d, AvgPool1d | |
| from torch.nn.utils import weight_norm, spectral_norm | |
| from torch import nn | |
| from modules.vocoder_blocks import * | |
| LRELU_SLOPE = 0.1 | |
| class DiscriminatorS(nn.Module): | |
| def __init__(self, use_spectral_norm=False): | |
| super(DiscriminatorS, self).__init__() | |
| norm_f = weight_norm if use_spectral_norm == False else spectral_norm | |
| self.convs = nn.ModuleList( | |
| [ | |
| norm_f(Conv1d(1, 128, 15, 1, padding=7)), | |
| norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)), | |
| norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)), | |
| norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)), | |
| norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)), | |
| norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)), | |
| norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), | |
| ] | |
| ) | |
| self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) | |
| def forward(self, x): | |
| fmap = [] | |
| for l in self.convs: | |
| x = l(x) | |
| x = F.leaky_relu(x, LRELU_SLOPE) | |
| fmap.append(x) | |
| x = self.conv_post(x) | |
| fmap.append(x) | |
| x = torch.flatten(x, 1, -1) | |
| return x, fmap | |
| class MultiScaleDiscriminator(nn.Module): | |
| def __init__(self, cfg): | |
| super(MultiScaleDiscriminator, self).__init__() | |
| self.cfg = cfg | |
| self.discriminators = nn.ModuleList( | |
| [ | |
| DiscriminatorS(use_spectral_norm=True), | |
| DiscriminatorS(), | |
| DiscriminatorS(), | |
| ] | |
| ) | |
| self.meanpools = nn.ModuleList( | |
| [AvgPool1d(4, 2, padding=2), AvgPool1d(4, 2, padding=2)] | |
| ) | |
| def forward(self, y, y_hat): | |
| y_d_rs = [] | |
| y_d_gs = [] | |
| fmap_rs = [] | |
| fmap_gs = [] | |
| for i, d in enumerate(self.discriminators): | |
| if i != 0: | |
| y = self.meanpools[i - 1](y) | |
| y_hat = self.meanpools[i - 1](y_hat) | |
| y_d_r, fmap_r = d(y) | |
| y_d_g, fmap_g = d(y_hat) | |
| y_d_rs.append(y_d_r) | |
| fmap_rs.append(fmap_r) | |
| y_d_gs.append(y_d_g) | |
| fmap_gs.append(fmap_g) | |
| return y_d_rs, y_d_gs, fmap_rs, fmap_gs | |
| class MultiScaleDiscriminator_JETS(nn.Module): | |
| def __init__(self): | |
| super(MultiScaleDiscriminator_JETS, self).__init__() | |
| self.discriminators = nn.ModuleList( | |
| [ | |
| DiscriminatorS(use_spectral_norm=True), | |
| DiscriminatorS(), | |
| DiscriminatorS(), | |
| ] | |
| ) | |
| self.meanpools = nn.ModuleList( | |
| [AvgPool1d(4, 2, padding=2), AvgPool1d(4, 2, padding=2)] | |
| ) | |
| def forward(self, y): | |
| y_d_rs = [] # p, y, groud-truth | |
| fmap_rs = [] | |
| for i, d in enumerate(self.discriminators): | |
| if i != 0: | |
| y = self.meanpools[i - 1](y) | |
| y_d_r, fmap_r = d(y) | |
| y_d_rs.append(y_d_r) | |
| fmap_rs.append(fmap_r) | |
| return y_d_rs, fmap_rs | |
| # fmap_rs is real, fmap_gs is generated. | |