| | |
|---|
| | | ) |
|---|
| | | 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" |
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| | | # 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.forward_chunk( |
|---|
| | | # 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.forward_chunk( |
|---|
| | | # 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.forward_chunk( |
|---|
| | | # 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_chunk( |
|---|
| | | self, |
|---|
| | | memory: torch.Tensor, |
|---|
