| | |
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| | | target_buffer_length: int = -1, |
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| | | inner_dim: int = 256, |
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| | | bias_encoder_type: str = 'lstm', |
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| | | use_decoder_embedding: bool = True, |
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| | | use_decoder_embedding: bool = False, |
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| | | crit_attn_weight: float = 0.0, |
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| | | crit_attn_smooth: float = 0.0, |
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| | | bias_encoder_dropout_rate: float = 0.0, |
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| | |
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| | | |
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| | | # 1. Forward decoder |
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| | | decoder_outs = self.decoder( |
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| | | encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, contextual_info=contextual_info, ret_attn=(ideal_attn is not None) |
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| | | encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, contextual_info=contextual_info |
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| | | ) |
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| | | decoder_out, _, attn = decoder_outs[0], decoder_outs[1], decoder_outs[2] |
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| | | decoder_out, _ = decoder_outs[0], decoder_outs[1] |
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| | | ''' |
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| | | if self.crit_attn_weight > 0 and attn.shape[-1] > 1: |
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| | | ideal_attn = ideal_attn + self.crit_attn_smooth / (self.crit_attn_smooth + 1.0) |
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| | | attn_non_blank = attn[:,:,:,:-1] |
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| | |
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| | | loss_ideal = self.attn_loss(attn_non_blank.max(1)[0], ideal_attn_non_blank.to(attn.device)) |
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| | | else: |
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| | | loss_ideal = None |
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| | | |
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| | | ''' |
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| | | loss_ideal = None |
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| | | |
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| | | if decoder_out_1st is None: |
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| | | decoder_out_1st = decoder_out |
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| | | # 2. Compute attention loss |
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| | |
|---|
| | | input_mask_expand_dim, 0) |
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| | | return sematic_embeds * tgt_mask, decoder_out * tgt_mask |
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| | | |
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| | | def cal_decoder_with_predictor_with_hwlist_advanced(self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, hw_list=None): |
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| | | def cal_decoder_with_predictor(self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, hw_list=None): |
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| | | if hw_list is None: |
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| | | hw_list = [torch.Tensor([1]).long().to(encoder_out.device)] # empty hotword list |
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| | | hw_list_pad = pad_list(hw_list, 0) |
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| | |
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| | | hw_embed = self.bias_embed(hw_list_pad) |
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| | | hw_embed, (h_n, _) = self.bias_encoder(hw_embed) |
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| | | else: |
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| | | # hw_list = hw_list[1:] + [hw_list[0]] # reorder |
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| | | hw_lengths = [len(i) for i in hw_list] |
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| | | hw_list_pad = pad_list([torch.Tensor(i).long() for i in hw_list], 0).to(encoder_out.device) |
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| | | if self.use_decoder_embedding: |
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| | |
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| | | if _h_n is not None: |
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| | | h_n = _h_n |
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| | | hw_embed = h_n.repeat(encoder_out.shape[0], 1, 1) |
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| | | # import pdb; pdb.set_trace() |
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| | | |
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| | | decoder_outs = self.decoder( |
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| | | encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, contextual_info=hw_embed |
|---|
