Created
September 16, 2022 13:45
-
-
Save shark8me/dc13cd39e7d16df4bb89c529cd5b4e52 to your computer and use it in GitHub Desktop.
Default convolution stack for AMT
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| class ConvStack2d(nn.Module): | |
| def __init__(self, input_features, output_features): | |
| super().__init__() | |
| # input is batch_size * 1 channel * frames * input_features | |
| self.cnn = nn.Sequential( | |
| # layer 0 | |
| nn.Conv2d(1, output_features // 16, (3, 3), padding=1), | |
| nn.BatchNorm2d(output_features // 16), | |
| nn.ReLU(), | |
| # layer 1 | |
| nn.Conv2d(output_features // 16, output_features // 16, (3, 3), padding=1), | |
| nn.BatchNorm2d(output_features // 16), | |
| nn.ReLU(), | |
| # layer 2 | |
| nn.MaxPool2d((1, 2)), | |
| nn.Dropout(0.25), | |
| nn.Conv2d(output_features // 16, output_features // 8, (3, 3), padding=1), | |
| nn.BatchNorm2d(output_features // 8), | |
| nn.ReLU(), | |
| # layer 3 | |
| nn.MaxPool2d((1, 2)), | |
| nn.Dropout(0.25), | |
| ) | |
| self.fc = nn.Sequential( | |
| nn.Linear((output_features // 8) * (input_features // 4), output_features), | |
| nn.Linear(output_features,1) | |
| ) | |
| self.fc2 = nn.Sequential( | |
| nn.Linear(num_steps,output_features) | |
| ) | |
| def forward(self, mel): | |
| x = mel.view(mel.size(0), 1, mel.size(1), mel.size(2)) | |
| x = self.cnn(x) | |
| x = x.transpose(1, 2).flatten(-2) | |
| x = self.fc(x) | |
| x= torch.squeeze(x) | |
| x = self.fc2(x) | |
| return x |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment