def forward()

in src/diarizers/models/pyannet.py [0:0]

• 14 lines of code
• 6 McCabe index (conditional complexity)

    def forward(self, waveforms: torch.Tensor) -> torch.Tensor:
        """Pass forward

        Parameters
        ----------
        waveforms : (batch, channel, sample)

        Returns
        -------
        scores : (batch, frame, classes)
        """

        outputs = self.sincnet(waveforms)

        if self.hparams.lstm["monolithic"]:
            outputs, _ = self.lstm(rearrange(outputs, "batch feature frame -> batch frame feature"))
        else:
            outputs = rearrange(outputs, "batch feature frame -> batch frame feature")
            for i, lstm in enumerate(self.lstm):
                outputs, _ = lstm(outputs)
                if i + 1 < self.hparams.lstm["num_layers"]:
                    outputs = self.dropout(outputs)

        if self.hparams.linear["num_layers"] > 0:
            for linear in self.linear:
                outputs = F.leaky_relu(linear(outputs))

        return self.activation(self.classifier(outputs))