Method forward

funasr/models/sanm/encoder.py:392–461

Embed positions in tensor. Args: xs_pad: input tensor (B, L, D) ilens: input length (B) prev_states: Not to be used now. Returns: position embedded tensor and mask

(
        self,
        xs_pad: torch.Tensor,
        ilens: torch.Tensor,
        prev_states: torch.Tensor = None,
        ctc: CTC = None,
    )

        return self._output_size

    def forward(
        self,
        xs_pad: torch.Tensor,
        ilens: torch.Tensor,
        prev_states: torch.Tensor = None,
        ctc: CTC = None,
    ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
        """Embed positions in tensor.

        Args:
            xs_pad: input tensor (B, L, D)
            ilens: input length (B)
            prev_states: Not to be used now.
        Returns:
            position embedded tensor and mask
        """
        masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device)
        xs_pad = xs_pad * self.output_size() ** 0.5
        if self.embed is None:
            xs_pad = xs_pad
        elif (
            isinstance(self.embed, Conv2dSubsampling)
            or isinstance(self.embed, Conv2dSubsampling2)
            or isinstance(self.embed, Conv2dSubsampling6)
            or isinstance(self.embed, Conv2dSubsampling8)
        ):
            short_status, limit_size = check_short_utt(self.embed, xs_pad.size(1))
            if short_status:
                raise TooShortUttError(
                    f"has {xs_pad.size(1)} frames and is too short for subsampling "
                    + f"(it needs more than {limit_size} frames), return empty results",
                    xs_pad.size(1),
                    limit_size,
                )
            xs_pad, masks = self.embed(xs_pad, masks)
        else:
            xs_pad = self.embed(xs_pad)

        # xs_pad = self.dropout(xs_pad)
        encoder_outs = self.encoders0(xs_pad, masks)
        xs_pad, masks = encoder_outs[0], encoder_outs[1]
        intermediate_outs = []
        if len(self.interctc_layer_idx) == 0:
            encoder_outs = self.encoders(xs_pad, masks)
            xs_pad, masks = encoder_outs[0], encoder_outs[1]
        else:
            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]

                if layer_idx + 1 in self.interctc_layer_idx:
                    encoder_out = xs_pad

                    # intermediate outputs are also normalized
                    if self.normalize_before:
                        encoder_out = self.after_norm(encoder_out)

                    intermediate_outs.append((layer_idx + 1, encoder_out))