| | |
|---|
| | | cache["feats"] = overlap_feats[:, -(cache["chunk_size"][0] + cache["chunk_size"][2]):, :] |
|---|
| | | return overlap_feats |
|---|
| | | |
|---|
| | | #def forward_chunk(self, |
|---|
| | | # xs_pad: torch.Tensor, |
|---|
| | | # ilens: torch.Tensor, |
|---|
| | | # cache: dict = None, |
|---|
| | | # ctc: CTC = None, |
|---|
| | | # ): |
|---|
| | | # xs_pad *= self.output_size() ** 0.5 |
|---|
| | | # if self.embed is None: |
|---|
| | | # xs_pad = xs_pad |
|---|
| | | # else: |
|---|
| | | # xs_pad = self.embed(xs_pad, cache) |
|---|
| | | # if cache["tail_chunk"]: |
|---|
| | | # xs_pad = to_device(cache["feats"], device=xs_pad.device) |
|---|
| | | # else: |
|---|
| | | # xs_pad = self._add_overlap_chunk(xs_pad, cache) |
|---|
| | | # encoder_outs = self.encoders0(xs_pad, None, None, None, None) |
|---|
| | | # xs_pad, masks = encoder_outs[0], encoder_outs[1] |
|---|
| | | # intermediate_outs = [] |
|---|
| | | # if len(self.interctc_layer_idx) == 0: |
|---|
| | | # encoder_outs = self.encoders(xs_pad, None, None, None, None) |
|---|
| | | # 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, None, None, None, None) |
|---|
| | | # 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)) |
|---|
| | | |
|---|
| | | # if self.interctc_use_conditioning: |
|---|
| | | # ctc_out = ctc.softmax(encoder_out) |
|---|
| | | # xs_pad = xs_pad + self.conditioning_layer(ctc_out) |
|---|
| | | |
|---|
| | | # if self.normalize_before: |
|---|
| | | # xs_pad = self.after_norm(xs_pad) |
|---|
| | | |
|---|
| | | # if len(intermediate_outs) > 0: |
|---|
| | | # return (xs_pad, intermediate_outs), None, None |
|---|
| | | # return xs_pad, ilens, None |
|---|
| | | |
|---|
| | | |
|---|
| | | def forward_chunk(self, |
|---|
| | | xs_pad: torch.Tensor, |
|---|
| | | ilens: torch.Tensor, |
|---|
