| | |
|---|
| | | |
|---|
| | | hotword_pad = kwargs.get("hotword_pad") |
|---|
| | | hotword_lengths = kwargs.get("hotword_lengths") |
|---|
| | | dha_pad = kwargs.get("dha_pad") |
|---|
| | | seaco_label_pad = kwargs.get("seaco_label_pad") |
|---|
| | | |
|---|
| | | batch_size = speech.shape[0] |
|---|
| | | # for data-parallel |
|---|
| | |
|---|
| | | ys_lengths, |
|---|
| | | hotword_pad, |
|---|
| | | hotword_lengths, |
|---|
| | | dha_pad, |
|---|
| | | seaco_label_pad, |
|---|
| | | ) |
|---|
| | | if self.train_decoder: |
|---|
| | | loss_att, acc_att = self._calc_att_loss( |
|---|
| | |
|---|
| | | encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to( |
|---|
| | | encoder_out.device) |
|---|
| | | predictor_outs = self.predictor(encoder_out, None, encoder_out_mask, ignore_id=self.ignore_id) |
|---|
| | | if len(predictor_outs) == 4: |
|---|
| | | pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = predictor_outs |
|---|
| | | else: |
|---|
| | | pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index, pre_token_length2 = predictor_outs |
|---|
| | | return pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index |
|---|
| | | return predictor_outs[:4] |
|---|
| | | |
|---|
| | | def _calc_seaco_loss( |
|---|
| | | self, |
|---|
| | |
|---|
| | | ys_lengths: torch.Tensor, |
|---|
| | | hotword_pad: torch.Tensor, |
|---|
| | | hotword_lengths: torch.Tensor, |
|---|
| | | dha_pad: torch.Tensor, |
|---|
| | | seaco_label_pad: torch.Tensor, |
|---|
| | | ): |
|---|
| | | # predictor forward |
|---|
| | | encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to( |
|---|
| | |
|---|
| | | dec_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, decoder_out, ys_lengths) |
|---|
| | | merged = self._merge(cif_attended, dec_attended) |
|---|
| | | dha_output = self.hotword_output_layer(merged[:, :-1]) # remove the last token in loss calculation |
|---|
| | | loss_att = self.criterion_seaco(dha_output, dha_pad) |
|---|
| | | loss_att = self.criterion_seaco(dha_output, seaco_label_pad) |
|---|
| | | return loss_att |
|---|
| | | |
|---|
| | | def _seaco_decode_with_ASF(self, |
|---|
