Method forward_chunk

funasr/models/scama/decoder.py:160–200

Compute decoded features. Args: tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size). tgt_mask (torch.Tensor): Mask for input tensor (#batch, maxlen_out). memory (torch.Tensor): Encoded memory, float32 (#batch, maxlen_in, size). memory_

(
        self, tgt, memory, fsmn_cache=None, opt_cache=None, chunk_size=None, look_back=0
    )

        return x, tgt_mask, memory, memory_mask, cache

    def forward_chunk(
        self, tgt, memory, fsmn_cache=None, opt_cache=None, chunk_size=None, look_back=0
    ):
        """Compute decoded features.

        Args:
            tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size).
            tgt_mask (torch.Tensor): Mask for input tensor (#batch, maxlen_out).
            memory (torch.Tensor): Encoded memory, float32 (#batch, maxlen_in, size).
            memory_mask (torch.Tensor): Encoded memory mask (#batch, maxlen_in).
            cache (List[torch.Tensor]): List of cached tensors.
                Each tensor shape should be (#batch, maxlen_out - 1, size).

        Returns:
            torch.Tensor: Output tensor(#batch, maxlen_out, size).
            torch.Tensor: Mask for output tensor (#batch, maxlen_out).
            torch.Tensor: Encoded memory (#batch, maxlen_in, size).
            torch.Tensor: Encoded memory mask (#batch, maxlen_in).

        """
        residual = tgt
        if self.normalize_before:
            tgt = self.norm1(tgt)
        tgt = self.feed_forward(tgt)

        x = tgt
        if self.self_attn:
            if self.normalize_before:
                tgt = self.norm2(tgt)
            x, fsmn_cache = self.self_attn(tgt, None, fsmn_cache)
            x = residual + self.dropout(x)

        if self.src_attn is not None:
            residual = x
            if self.normalize_before:
                x = self.norm3(x)

            x, opt_cache = self.src_attn.forward_chunk(x, memory, opt_cache, chunk_size, look_back)
            x = residual + x

        return x, memory, fsmn_cache, opt_cache


@tables.register("decoder_classes", "FsmnDecoderSCAMAOpt")