Method forward

model.py:546–577

Embed positions in tensor.

(
        self,
        xs_pad: torch.Tensor,
        ilens: torch.Tensor,
    )

        return self._output_size

    def forward(
        self,
        xs_pad: torch.Tensor,
        ilens: torch.Tensor,
    ):
        """Embed positions in tensor."""
        masks = sequence_mask(ilens, device=ilens.device)[:, None, :]

        xs_pad *= self.output_size() ** 0.5

        xs_pad = self.embed(xs_pad)

        # forward encoder1
        for layer_idx, encoder_layer in enumerate(self.encoders0):
            encoder_outs = encoder_layer(xs_pad, masks)
            xs_pad, masks = encoder_outs[0], encoder_outs[1]

        for layer_idx, encoder_layer in enumerate(self.encoders):
            encoder_outs = encoder_layer(xs_pad, masks)
            xs_pad, masks = encoder_outs[0], encoder_outs[1]

        xs_pad = self.after_norm(xs_pad)

        # forward encoder2
        olens = masks.squeeze(1).sum(1).int()

        for layer_idx, encoder_layer in enumerate(self.tp_encoders):
            encoder_outs = encoder_layer(xs_pad, masks)
            xs_pad, masks = encoder_outs[0], encoder_outs[1]

        xs_pad = self.tp_norm(xs_pad)
        return xs_pad, olens


@tables.register("model_classes", "SenseVoiceSmall")