| | |
| | | from funasr.modules.positionwise_feed_forward import PositionwiseFeedForward |
| | | from funasr.export.models.modules.feedforward import PositionwiseFeedForward as PositionwiseFeedForward_export |
| | | |
| | | |
| | | class SANMEncoder(nn.Module): |
| | | def __init__( |
| | | self, |
| | |
| | | self.num_heads = model.encoders[0].self_attn.h |
| | | self.hidden_size = model.encoders[0].self_attn.linear_out.out_features |
| | | |
| | | def prepare_mask(self, mask): |
| | | def prepare_mask(self, mask, sub_masks): |
| | | mask_3d_btd = mask[:, :, None] |
| | | if len(mask.shape) == 2: |
| | | mask_4d_bhlt = 1 - mask[:, None, None, :] |
| | | elif len(mask.shape) == 3: |
| | | mask_4d_bhlt = 1 - mask[:, None, :] |
| | | mask_4d_bhlt = mask_4d_bhlt * -10000.0 |
| | | mask_4d_bhlt = (1 - sub_masks) * -10000.0 |
| | | |
| | | return mask_3d_btd, mask_4d_bhlt |
| | | |
| | | def forward(self, |
| | | speech: torch.Tensor, |
| | | speech_lengths: torch.Tensor, |
| | | vad_mask: torch.Tensor, |
| | | sub_masks: torch.Tensor, |
| | | ): |
| | | speech = speech * self._output_size ** 0.5 |
| | | mask = self.make_pad_mask(speech_lengths) |
| | | mask = self.prepare_mask(mask) |
| | | mask = self.prepare_mask(mask, sub_masks) |
| | | if self.embed is None: |
| | | xs_pad = speech |
| | | else: |
| | |
| | | encoder_outs = self.model.encoders0(xs_pad, mask) |
| | | xs_pad, masks = encoder_outs[0], encoder_outs[1] |
| | | |
| | | encoder_outs = self.model.encoders(xs_pad, mask) |
| | | xs_pad, masks = encoder_outs[0], encoder_outs[1] |
| | | # encoder_outs = self.model.encoders(xs_pad, mask) |
| | | for layer_idx, encoder_layer in enumerate(self.model.encoders): |
| | | if layer_idx == len(self.model.encoders) - 1: |
| | | mask = (mask[0], vad_mask) |
| | | encoder_outs = encoder_layer(xs_pad, mask) |
| | | xs_pad, masks = encoder_outs[0], encoder_outs[1] |
| | | |
| | | xs_pad = self.model.after_norm(xs_pad) |
| | | |