(self, spec_channels, gin_channels=0)
| 632 | """ |
| 633 | |
| 634 | def __init__(self, spec_channels, gin_channels=0): |
| 635 | super().__init__() |
| 636 | self.spec_channels = spec_channels |
| 637 | ref_enc_filters = [32, 32, 64, 64, 128, 128] |
| 638 | K = len(ref_enc_filters) |
| 639 | filters = [1] + ref_enc_filters |
| 640 | convs = [ |
| 641 | weight_norm( |
| 642 | nn.Conv2d( |
| 643 | in_channels=filters[i], |
| 644 | out_channels=filters[i + 1], |
| 645 | kernel_size=(3, 3), |
| 646 | stride=(2, 2), |
| 647 | padding=(1, 1), |
| 648 | ) |
| 649 | ) |
| 650 | for i in range(K) |
| 651 | ] |
| 652 | self.convs = nn.ModuleList(convs) |
| 653 | |
| 654 | out_channels = self.calculate_channels(spec_channels, 3, 2, 1, K) |
| 655 | self.gru = nn.GRU( |
| 656 | input_size=ref_enc_filters[-1] * out_channels, |
| 657 | hidden_size=256 // 2, |
| 658 | batch_first=True, |
| 659 | ) |
| 660 | self.proj = nn.Linear(128, gin_channels) |
| 661 | |
| 662 | def forward(self, inputs): |
| 663 | N = inputs.size(0) |
nothing calls this directly
no test coverage detected