| | |
| | | feats_dim=560, |
| | | model_name="encoder", |
| | | onnx: bool = True, |
| | | ctc_linear: nn.Module = None, |
| | | ): |
| | | super().__init__() |
| | | self.embed = model.embed |
| | |
| | | self.num_heads = model.encoders[0].self_attn.h |
| | | self.hidden_size = model.encoders[0].self_attn.linear_out.out_features |
| | | |
| | | self.ctc_linear = ctc_linear |
| | | |
| | | def prepare_mask(self, mask): |
| | | mask_3d_btd = mask[:, :, None] |
| | | if len(mask.shape) == 2: |
| | |
| | | def forward(self, speech: torch.Tensor, speech_lengths: torch.Tensor, online: bool = False): |
| | | if not online: |
| | | speech = speech * self._output_size**0.5 |
| | | |
| | | mask = self.make_pad_mask(speech_lengths) |
| | | mask = self.prepare_mask(mask) |
| | | if self.embed is None: |
| | |
| | | |
| | | xs_pad = self.model.after_norm(xs_pad) |
| | | |
| | | if self.ctc_linear is not None: |
| | | xs_pad = self.ctc_linear(xs_pad) |
| | | xs_pad = F.softmax(xs_pad, dim=2) |
| | | |
| | | return xs_pad, speech_lengths |
| | | |
| | | def get_output_size(self): |