| | |
|---|
| | | ) |
|---|
| | | return logp.squeeze(0), state |
|---|
| | | |
|---|
| | | def forward_chunk( |
|---|
| | | self, |
|---|
| | | memory: torch.Tensor, |
|---|
| | | tgt: torch.Tensor, |
|---|
| | | cache: dict = None, |
|---|
| | | ) -> Tuple[torch.Tensor, torch.Tensor]: |
|---|
| | | """Forward decoder. |
|---|
| | | |
|---|
| | | Args: |
|---|
| | | hs_pad: encoded memory, float32 (batch, maxlen_in, feat) |
|---|
| | | hlens: (batch) |
|---|
| | | ys_in_pad: |
|---|
| | | input token ids, int64 (batch, maxlen_out) |
|---|
| | | if input_layer == "embed" |
|---|
| | | input tensor (batch, maxlen_out, #mels) in the other cases |
|---|
| | | ys_in_lens: (batch) |
|---|
| | | Returns: |
|---|
| | | (tuple): tuple containing: |
|---|
| | | |
|---|
| | | x: decoded token score before softmax (batch, maxlen_out, token) |
|---|
| | | if use_output_layer is True, |
|---|
| | | olens: (batch, ) |
|---|
| | | """ |
|---|
| | | x = tgt |
|---|
| | | if cache["decode_fsmn"] is None: |
|---|
| | | cache_layer_num = len(self.decoders) |
|---|
| | | if self.decoders2 is not None: |
|---|
| | | cache_layer_num += len(self.decoders2) |
|---|
| | | new_cache = [None] * cache_layer_num |
|---|
| | | else: |
|---|
| | | new_cache = cache["decode_fsmn"] |
|---|
| | | for i in range(self.att_layer_num): |
|---|
| | | decoder = self.decoders[i] |
|---|
| | | x, tgt_mask, memory, memory_mask, c_ret = decoder( |
|---|
| | | x, None, memory, None, cache=new_cache[i] |
|---|
| | | ) |
|---|
| | | new_cache[i] = c_ret |
|---|
| | | |
|---|
| | | if self.num_blocks - self.att_layer_num > 1: |
|---|
| | | for i in range(self.num_blocks - self.att_layer_num): |
|---|
| | | j = i + self.att_layer_num |
|---|
| | | decoder = self.decoders2[i] |
|---|
| | | x, tgt_mask, memory, memory_mask, c_ret = decoder( |
|---|
| | | x, None, memory, None, cache=new_cache[j] |
|---|
| | | ) |
|---|
| | | new_cache[j] = c_ret |
|---|
| | | |
|---|
| | | for decoder in self.decoders3: |
|---|
| | | |
|---|
| | | x, tgt_mask, memory, memory_mask, _ = decoder( |
|---|
| | | x, None, memory, None, cache=None |
|---|
| | | ) |
|---|
| | | if self.normalize_before: |
|---|
| | | x = self.after_norm(x) |
|---|
| | | if self.output_layer is not None: |
|---|
| | | x = self.output_layer(x) |
|---|
| | | cache["decode_fsmn"] = new_cache |
|---|
| | | return x |
|---|
| | | |
|---|
| | | def forward_one_step( |
|---|
| | | self, |
|---|
| | | tgt: torch.Tensor, |
|---|
