fix contextualparaformer bias_embed

北念

2023-03-29 2b2653ae2b75d18c1d0f994072485bc403374f43

fix contextualparaformer bias_embed

 funasr/models/e2e_asr_paraformer.py

@@ -1085,6 +1085,7 @@
            inner_dim: int = 256,
            bias_encoder_type: str = 'lstm',
            label_bracket: bool = False,
            use_decoder_embedding: bool = False,
    ):
        assert check_argument_types()
        assert 0.0 <= ctc_weight <= 1.0, ctc_weight
@@ -1138,6 +1139,7 @@
            self.hotword_buffer = None
            self.length_record = []
            self.current_buffer_length = 0
        self.use_decoder_embedding = use_decoder_embedding

    def forward(
            self,
@@ -1279,7 +1281,10 @@
                    hw_list.append(hw_tokens)
        # padding
        hw_list_pad = pad_list(hw_list, 0)
        hw_embed = self.decoder.embed(hw_list_pad)
        if self.use_decoder_embedding:
            hw_embed = self.decoder.embed(hw_list_pad)
        else:
            hw_embed = self.bias_embed(hw_list_pad)
        hw_embed, (_, _) = self.bias_encoder(hw_embed)
        _ind = np.arange(0, len(hw_list)).tolist()
        # update self.hotword_buffer, throw a part if oversize
@@ -1395,13 +1400,19 @@
            # default hotword list
            hw_list = [torch.Tensor([self.sos]).long().to(encoder_out.device)]  # empty hotword list
            hw_list_pad = pad_list(hw_list, 0)
            hw_embed = self.bias_embed(hw_list_pad)
            if self.use_decoder_embedding:
                hw_embed = self.decoder.embed(hw_list_pad)
            else:
                hw_embed = self.bias_embed(hw_list_pad)
            _, (h_n, _) = self.bias_encoder(hw_embed)
            contextual_info = h_n.squeeze(0).repeat(encoder_out.shape[0], 1, 1)
        else:
            hw_lengths = [len(i) for i in hw_list]
            hw_list_pad = pad_list([torch.Tensor(i).long() for i in hw_list], 0).to(encoder_out.device)
            hw_embed = self.bias_embed(hw_list_pad)
            if self.use_decoder_embedding:
                hw_embed = self.decoder.embed(hw_list_pad)
            else:
                hw_embed = self.bias_embed(hw_list_pad)
            hw_embed = torch.nn.utils.rnn.pack_padded_sequence(hw_embed, hw_lengths, batch_first=True,
                                                               enforce_sorted=False)
            _, (h_n, _) = self.bias_encoder(hw_embed)