Created
October 12, 2022 17:13
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1-d conv stack for Automated Music Transcription
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| class ConvStack1d(nn.Module): | |
| def __init__(self, input_features, output_features): | |
| super().__init__() | |
| cqt_feats=217 | |
| op_divby=2 | |
| l1_out_channels= cqt_feats //op_divby | |
| self.cnn = nn.Sequential( | |
| # layer 0 | |
| nn.Conv1d(output_features, l1_out_channels, 3 , padding=1), | |
| nn.BatchNorm1d(l1_out_channels), | |
| nn.ReLU() | |
| ) | |
| l2_out_channels= l1_out_channels // 2 | |
| self.cnn2 = nn.Sequential( | |
| nn.Conv1d(l1_out_channels, l2_out_channels, 3 , padding=1), | |
| nn.BatchNorm1d(l2_out_channels), | |
| nn.ReLU(), | |
| nn.MaxPool2d((1, 2)), | |
| nn.Dropout(0.25), | |
| ) | |
| l3_out_channels= l2_out_channels // 2 | |
| self.cnn3 = nn.Sequential( | |
| nn.Conv1d(l2_out_channels, l3_out_channels, 3 , padding=1), | |
| nn.BatchNorm1d(l3_out_channels), | |
| nn.ReLU(), | |
| nn.MaxPool2d((1, 2)), | |
| nn.Dropout(0.25)) | |
| self.fc = nn.Sequential( | |
| nn.Linear(l2_out_channels * l3_out_channels, output_features) | |
| ) | |
| def forward(self, mel): | |
| x = mel.view(mel.size(0), 1, mel.size(1), mel.size(2)) | |
| x = torch.squeeze(x,dim=1) | |
| x = self.cnn(x) | |
| x = self.cnn2(x) | |
| x = self.cnn3(x) | |
| x = x.transpose(1, 2).flatten(-2) | |
| x = self.fc(x) | |
| return x |
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